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  • 1.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Lund University.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Analysis and characterization of a Video-on-Demand service workload2015In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015, ACM Digital Library, 2015, p. 189-200Conference paper (Refereed)
    Abstract [en]

    Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.

  • 2.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Dept. of Electrical and Information Technology, Lund University, Lund, Sweden.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control2012In: Proceedings of the 3rd workshop on Scientific Cloud Computing Date, Association for Computing Machinery (ACM), 2012, p. 31-40Conference paper (Refereed)
    Abstract [en]

    Elasticity is the ability of a cloud infrastructure to dynamically change theamount of resources allocated to a running service as load changes. We build anautonomous elasticity controller that changes the number of virtual machinesallocated to a service based on both monitored load changes and predictions offuture load. The cloud infrastructure is modeled as a G/G/N queue. This modelis used to construct a hybrid reactive-adaptive controller that quickly reactsto sudden load changes, prevents premature release of resources, takes intoaccount the heterogeneity of the workload, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed controller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when compared to a reactive controller. Ourcontroller also decreases the average number of queued requests by a factor of 3 for both traces, and reduces oscillations by a factor of 7 for the Web traces and 3 for the cluster traces. This comes at the expense of between 20% and 30% over-provisioning, as compared to a few percent for the reactive controller.

  • 3.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Rezaie, Ali
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Mehta, Amardeep
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Razroev, Stanislav
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Sjöstedt-de Luna, Sara
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Seleznjev, Oleg
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    How will your workload look like in 6 years?: Analyzing Wikimedia's workload2014In: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014) / [ed] Lisa O’Conner, IEEE Computer Society, 2014, p. 349-354Conference paper (Refereed)
    Abstract [en]

    Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.

  • 4.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Seleznjev, Oleg
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Sjöstedt-de Luna, Sara
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Measuring cloud workload burstiness2014In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), IEEE conference proceedings, 2014, p. 566-572Conference paper (Refereed)
    Abstract [en]

    Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, we review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for Cloud systems where various workloads are co-hosted on the same platform. We discuss Sample Entropy (SampEn), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for Cloud workloads.

  • 5.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An adaptive hybrid elasticity controller for cloud infrastructures2012In: 2012 IEEE Network operations and managent symposium (NOMS), IEEE Communications Society, 2012, p. 204-212Conference paper (Refereed)
    Abstract [en]

    Cloud elasticity is the ability of the cloud infrastructure to rapidly change the amount of resources allocated to a service in order to meet the actual varying demands on the service while enforcing SLAs. In this paper, we focus on horizontal elasticity, the ability of the infrastructure to add or remove virtual machines allocated to a service deployed in the cloud. We model a cloud service using queuing theory. Using that model we build two adaptive proactive controllers that estimate the future load on a service. We explore the different possible scenarios for deploying a proactive elasticity controller coupled with a reactive elasticity controller in the cloud. Using simulation with workload traces from the FIFA world-cup web servers, we show that a hybrid controller that incorporates a reactive controller for scale up coupled with our proactive controllers for scale down decisions reduces SLA violations by a factor of 2 to 10 compared to a regression based controller or a completely reactive controller.

  • 6.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Lund University.
    WAC: A Workload analysis and classification tool for automatic selection of cloud auto-scaling methodsManuscript (preprint) (Other academic)
    Abstract [en]

    Autoscaling algorithms for elastic cloud infrastructures dynami-cally change the amount of resources allocated to a service ac-cording to the current and predicted future load. Since there areno perfect predictors, no single elasticity algorithm is suitable foraccurate predictions of all workloads. To improve the quality ofworkload predictions and increase the Quality-of-Service (QoS)guarantees of a cloud service, multiple autoscalers suitable for dif-ferent workload classes need to be used. In this work, we intro-duce WAC, a Workload Analysis and Classification tool that as-signs workloads to the most suitable elasticity autoscaler out of aset of pre-deployed autoscalers. The workload assignment is basedon the workload characteristics and a set of user-defined Business-Level-Objectives (BLO). We describe the tool design and its maincomponents. We implement WAC and evaluate its precision us-ing various workloads, BLO combinations and state-of-the-art au-toscalers. Our experiments show that, when the classifier is tunedcarefully, WAC assigns between 87% and 98.3% of the workloadsto the most suitable elasticity autoscaler.

  • 7.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Department of Electrical and Information Technology, Lund University, Lund, Sweden.
    Workload Classification for Efficient Auto-Scaling of Cloud Resources2013Manuscript (preprint) (Other academic)
    Abstract [en]

    Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfect predictor, and since different applications’ workloads have different characteristics, no single elasticity algorithm is suitable for future predictions for all workloads. In this work, we introduceWAC, aWorkload Analysis and Classification tool that analyzes workloads and assigns them to the most suitable elasticity controllers based on the workloads’ characteristics and a set of business level objectives.

    WAC has two main components, the analyzer and the classifier. The analyzer analyzes workloads to extract some of the features used by the classifier, namely, workloads’ autocorrelations and sample entropies which measure the periodicity and the burstiness of the workloads respectively. These two features are used with the business level objectives by the clas-sifier as the features used to assign workloads to elasticity controllers. We start by analyzing 14 real workloads available from different applications. In addition, a set of 55 workloads is generated to test WAC on more workload configurations. We implement four state of the art elasticity algorithms. The controllers are the classes to which the classifier assigns workloads. We use a K nearest neighbors classifier and experiment with different workload combinations as training and test sets. Our experi-ments show that, when the classifier is tuned carefully, WAC correctly classifies between 92% and 98.3% of the workloads to the most suitable elasticity controller.

  • 8.
    Armstrong, Django
    et al.
    School of Computing, University of Leeds.
    Djemame, Karin
    School of Computing, University of Leeds.
    Nair, Srijith
    British Telecom.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ziegler, Wolfgang
    Fraunhofer SCAI.
    Towards a Contextualization Solution for Cloud Platform Services2011In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science, IEEE Computer Society, 2011, p. 328-331Conference paper (Refereed)
    Abstract [en]

    We propose a cloud contextualization mechanism which operates in two stages, contextualization of VM images prior to service deployment (PaaS level) and self-contextualization of VM instances created from the image (IaaS level). The contextualization tools are implemented as part of the OPTIMIS Toolkit, a set of software components for simplified management of cloud services and infrastructures. We present the architecture of our contextualization tools and the feasibility of our contextualization mechanism is demonstrated in a three-tier web application scenario. Preliminary performance results suggest acceptable performance and scalability.

  • 9.
    Armstrong, Django
    et al.
    University of Leeds.
    Espling, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Contextualization: dynamic configuration of virtual machines2015In: Journal of Cloud Computing - Advances, Systems and Applications, ISSN 2192-113X, Vol. 4, no 17Article in journal (Refereed)
    Abstract [en]

    New VM instances are created from static templates that contain the basic configuration of the VM to achieve elasticity with regards to capacity. Instance specific settings can be injected into the VM during the deployment phase through means of contextualization. So far this is limited to a single data source and data remains static throughout the lifecycle of the VM.

    We present a layered approach to contextualization that supports different classes of contextualization data available from several sources. The settings are made available to the VM through virtual devices. Inside each VM data from different classes are layered on top of each other to create a unified file hierarchy.

    Context data can be modified during runtime by updating the contents of the virtual devices, making our approach the first contextualization approach to natively support recontextualization. Recontextualization enables runtime reconfiguration of an executing service and can act as a trigger and key enabler of self-* techniques. This trigger provides a service with a mechanism to adapt or optimize itself in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are illustrated in an example with a distributed file system, demonstrating the feasibility of our approach.

  • 10.
    Armstrong, Django
    et al.
    University of Leeds.
    Espling, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Runtime virtual machine recontextualization for clouds2013In: Euro-Par 2012: Parallel Processing Workshops / [ed] Ioannis Caragiannis et al., Springer Berlin/Heidelberg, 2013, Vol. 7640, p. 567-576Conference paper (Refereed)
    Abstract [en]

    We introduce and define the concept of recontextualization for cloud applications by extending contextualization, i.e. the dynamic configuration of virtual machines (VM) upon initialization, with autonomous updates during runtime. Recontextualization allows VM images and instances to be dynamically re-configured without restarts or downtime, and the concept is applicable to all aspects of configuring a VM from virtual hardware to multi-tier software stacks. Moreover, we propose a runtime cloud recontextualization mechanism based on virtual device management that enables recontextualization without the need to customize the guest VM. We illustrate our concept and validate our mechanism via a use case demonstration: the reconfiguration of a cross-cloud migratable monitoring service in a dynamic cloud environment. We discuss the details of the interoperable recontextualization mechanism, its architecture and demonstrate a proof of concept implementation. A performance evaluation illustrates the feasibility of the approach and shows that the recontextualization mechanism performs adequately with an overhead of 18% of the total migration time.

  • 11. Beco, S
    et al.
    Maraschini, A
    Pacini, F
    Biran, O
    Breitgand, O
    Meth, K
    Rochwerger, B
    Salant, E
    Silvera, E
    Tal, S
    Wolfsthal, Y
    Yehuda, M
    Caceres, J
    Hierro, J
    Emmerich, W
    Galis, A
    Edblom, Lennart
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Henriksson, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hohl, A
    Levy, E
    Sampaio, A
    Scheuermann, B
    Wusthoff, M
    Latanicki, J
    Lopez, G
    Marin-Frisonroche, J
    Dorr, A
    Ferstl, F
    Huedo, E
    Llorente, I
    Montero, R
    Massonet, P
    Naqvi, S
    Dallons, G
    Pezz, M
    Puliafito, A
    Ragusa, C
    Scarpa, M
    Muscella, S
    Cloud Computing and RESERVOIR project2009In: Nuovo Cimento C, ISSN ISSN 1124-1896, Vol. 32, no 2, p. 99-103Article in journal (Refereed)
  • 12. Ben Yehuda, M.
    et al.
    Biran, O.
    Breitgand, D.
    Meth, K.
    Rochwerger, B.
    Salant, E.
    Silvera, E.
    Tal, S.
    Wolfsthal, Y.
    Cáceres, J.
    Hierro, J.
    Emmerich, W.
    Galis, A.
    Edblom, Lennart
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Henriksson, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hohl, A.
    Levy, E.
    Sampaio, A.
    Scheuermann, B.
    Wusthoff, M.
    Latanicki, J.
    Lopez, G.
    Marin-Frisonroche, J.
    Dörr, A.
    Ferstl, F.
    Beco, S.
    Pacini, F.
    Llorente, I.
    Montero, R.
    Huedo, E.
    Massonet, P.
    Naqvi, S.
    Dallons, G.
    Pezzé, M.
    Puliato, A.
    Ragusa, C.
    Scarpa, M.
    Muscella, S.
    RESERVOIR: An ICT Infrastructure for Reliable and Effective Delivery of Services as Utilities2008Report (Other academic)
  • 13.
    Berglund, Ann-Charlotte
    et al.
    Linnaeus Centre for Bioinformatics, Uppsala Universitet.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sandman, Björn
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Combining local and grid resources in scientific workflows (for Bioinformatics)2009Conference paper (Refereed)
    Abstract [en]

    We examine some issues that arise when using both local and Gridresources in scientific workflows. Our previous work addresses and illustratesthe benefits of a light-weight and generic workflow engine that manages andoptimizes Grid resource usage. Extending on this effort, we hereillustrate how a client tool for bioinformatics applications employs the engine tointerface with Grid resources. We also explore how to define data flowsthat transparently integrates local and Grid subworkflows. In addition, the benefits of parameter sweep workflows are examined and a means for describing this type of workflows in an abstract and concise manner is introduced. Finally, the above mechanisms are employed to perform an orthology detection analysis.

  • 14. Breitgand, D.
    et al.
    Maraschini, A.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Policy-Driven Service Placement Optimization in Federated Clouds2011Report (Other academic)
    Abstract [en]

    Efficient provisioning of elastic services constitutes a significant management challenge for cloud computing providers. We consider a federated cloud paradigm, where one cloud can subcontract workloads to partnering clouds to meet peaks in demand without costly over-provisioning. We propose a model for service placement in federated clouds to maximize profit while protecting Quality of Service (QoS) as specified in the Service Level Agreements (SLA) of the workloads. Our contributions include an Integer Linear Program (ILP) formulation of the generalized federated placement problem and application of this problem to load balancing and consolidation within a cloud, as well as for cost minimization for remote placement in partnering clouds. We also provide a 2-approximation algorithm based on a greedy rounding of a Linear Program (LP) relaxation of the problem. We implement our proposed approach in the context of the RESERVOIR architecture.

  • 15.
    Desmeurs, David
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    SimScale GmbH.
    Papadopoulos, Alessandro
    Lund University.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Event-Driven Application Brownout: Reconciling High Utilization and Low Tail Response Times2015In: 2015 International Conference on Cloud and Autonomic Computing (ICCAC), New York: IEEE Computer Society, 2015, p. 1-12Conference paper (Refereed)
    Abstract [en]

    Data centers currently waste a lot of energy, due to lack of energy proportionality and low resource utilization, the latter currently being necessary to ensure application responsiveness. To address the second concern we propose a novel application-level technique that we call event-driven Brownout. For each request, i.e., in an event-driven manner, the application can execute some optional code that is not required for correct operation but desirable for user experience, and does so only if the number of pending client requests is below a given threshold. We propose several autonomic algorithms, based on control theory and machine learning, to automatically tune this threshold based on measured application 95th percentile response times. We evaluate our approach using the RUBiS benchmark which shows a 11-fold improvement in maintaining response-time close to a set-point at high utilization compared to competing approaches. Our contribution is opening the path to more energy efficient data-centers, by allowing applications to keep response times close to a set-point even at high resource utilization.

  • 16. Dürango, Jonas
    et al.
    Tärneberg, William
    Tomas, Luis
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Maggio, Martina
    A control theoretical approach to non-intrusive geo-replication for cloud services2016In: 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), IEEE, 2016, p. 1649-1656Conference paper (Refereed)
    Abstract [en]

    Complete data center failures may occur due to disastrous events such as earthquakes or fires. To attain robustness against such failures and reduce the probability of data loss, data must be replicated in another data center sufficiently geographically separated from the original data center. Implementing geo-replication is expensive as every data update operation in the original data center must be replicated in the backup. Running the application and the replication service in parallel is cost effective but creates a trade-off between potential replication consistency and data loss and reduced application performance due to network resource contention. We model this trade-off and provide a control-theoretical solution based on Model Predictive Control to dynamically allocate network bandwidth to accommodate the objectives of both replication and application data streams. We evaluate our control solution through simulations emulating the individual services, their traffic flows, and the shared network resource. The MPC solution is able to maintain a consistent performance over periods of persistent overload, and is quickly able to indiscriminately recover once the system return to a stable state. Additionally, the MPC balances the two objectives of consistency and performance according to the proportions specified in the objective function.

  • 17.
    Elmroth, E.
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Gardfjall, P.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, J.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ali-Eldin, A.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    L., Larsson
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    METHOD, NODE AND COMPUTER PROGRAM FOR ENABLING AUTOMATIC ADAPTATION OF RESOURCE UNITS2015Patent (Other (popular science, discussion, etc.))
  • 18.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N). Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Gardfjäll, Peter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Norberg, Arvid
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Designing general, composable, and middleware-independent Grid infrastructure tools for multi-tiered job management2007In: Towards Next Generation Grids / [ed] T. Priol and M. Vaneschi, Springer-Verlag , 2007, p. 175-184Conference paper (Refereed)
    Abstract [en]

    We propose a multi-tiered architecture for middleware-independent Grid job management. The architecture consists of a number of services for well-defined tasks in the job management process, offering complete user-level isolation of servicecapabilities, multiple layers of abstraction, control, and fault tolerance. The middleware abstraction layer comprises components for targeted job submission, job control and resource discovery. The brokered job submission layer offers a Grid view on resources, including functionality for resource brokering and submission of jobs to selected resources. The reliable job submission layer includes components for fault tolerant execution of individual jobs and groups of independentjobs, respectively. The architecture is proposed as a composable set of tools rather than a monolithic solution, allowing users to select the individual components of interest. The prototype presented is implemented using the Globus Toolkit 4, integrated with the Globus Toolkit 4 and NorduGrid/ARC middlewares and based on existing and emerging Grid standards. A performance evaluation reveals that the overhead for resource discovery, brokering, middleware-specific format conversions, job monitoring, fault tolerance, and management of individual and groups of jobs is sufficiently small to motivate the use of the framework.

  • 19.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N). Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Gardfjäll, Peter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An Advanced Grid Computing Course for Application and Infrastructure Developers2005In: 2005 IEEE International Symposium on Cluster Computing and the Grid, USA: IEEE Computer Society Press , 2005, p. 43-50Conference paper (Refereed)
    Abstract [en]

    This contribution presents our experiences from developing an advanced course in grid computing, aimed at application and infrastructure developers. The course was intended for computer science students with extensive programming experience and previous knowledge of distributed systems, parallel computing, computer networking, and security. The presentation includes brief presentations of all topics covered in the course, a list of the literature used, and descriptions of the mandatory computer assignments performed using Globus Toolkit 2 and 3. A summary of our experiences from the course and some suggestions for future directions concludes the presentation.

  • 20.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, High Performance Compting Center North (HPC2N). Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A light-weight Grid workflow execution service enabling client and middleware independence2008In: Parallel Processing and Applied Mathematics: 7th International Conference on Parallel Processing and Applied Mathematics (PPAM 2007), Springer-Verlag , 2008, p. 754-761Conference paper (Refereed)
    Abstract [en]

    We present a generic and light-weight Grid workflow execution engine made available as a Grid service. A long-term goal is to facilitate the rapid development of application-oriented end-user workflow tools, while providing a high degree of Grid middleware-independence. The workflow engine is designed for workflow execution, independent of client tools for workflow definition. A flexible plugin-structure for middleware-integration provides a strict separation of the workflow execution and the processing of individual tasks, such as computational jobs or file transfers. The light-weight design is achieved by focusing on the generic workflow execution components and by leveraging state-of-the art Grid technology, e.g., for state management. The current prototype is implemented using the Globus Toolkit 4 (GT4) Java WS Core and has support for executing workflows produced by Karajan. It also includes plugins for task execution with GT4 as well as a high-level Grid job management framework.

  • 21.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Three fundamental dimensions of scientific workflow interoperability: model of computation, language, and execution environment2010In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 26, no 2, p. 245-256Article in journal (Refereed)
    Abstract [en]

    We investigate interoperability aspects of scientific workflow systems and argue that the workflow execution environment, the model of computation (MoC), and the workflow language form three dimensions that must be considered depending on the type of interoperability sought: at the activity, sub-workflow, or workflow levels. With a focus on the problems that affect interoperability, we illustrate how these issues are tackled by current scientific workflows as well as how similar problems have been addressed in related areas. Our long-term objective is to achieve (logical) interoperability between workflow systems operating under different MoCs, using distinct language features, and sharing activities running on different execution environments.

  • 22.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, High Performance Compting Center North (HPC2N). Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Designing service-based resource management tools for a healthy grid ecosystem2008In: Parallel processing and applied mathematics: 7th International Conference on Parallel Processing and Applied Mathematics, Springer-Verlag , 2008, p. 259-270Conference paper (Refereed)
    Abstract [en]

    We present an approach for development of Grid resource management tools, where we put into practice internationally established high-level views of future Grid architectures. The approach addresses fundamental Grid challenges and strives towards a future vision of the Grid where capabilities are made available as independent and dynamically assembled utilities, enabling run-time changes in the structure, behavior, and location of software. The presentation is made in terms of design heuristics, design patterns, and quality attributes, and is centered around the key concepts of co-existence, composability, adoptability, adaptability, changeability, and interoperability. The practical realization of the approach is illustrated by five case studies (recently developed Grid tools) high-lighting the most distinct aspects of these key concepts for each tool. The approach contributes to a healthy Grid ecosystem that promotes a natural selection of “surviving” components through competition, innovation, evolution, and diversity. In conclusion, this environment facilitates the use and composition of components on a per-component basis.

  • 23.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sandgren, Åke
    Umeå University, Faculty of Science and Technology, High Performance Compting Center North (HPC2N).
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Resource Management for Early Production Grids2003Report (Other (popular science, discussion, etc.))
    Abstract [en]

    This contribution presents the ongoing development of a resource managerfor use in early production grids. Even though our main focus is todevelop a stable brokering facility for current production grids, we alsoaddress features needed in further improved resource managers for futureenhanced grid infrastructures. The primary target environment is theNorduGrid platform, comprising around 20 parallel systems in 5 countries,available for production grid jobs 24 hours a day. Application characteristicsconsidered include serial, parallel, and coordinated multi-resourcejobs running in sequence or in parallel, all types in either interactive ornon-interactive mode. The brokering process aims to minimize the timeto delivery for each individual job and is based on a number of new featuresincluding reservation capability, information about currently usedor reserved capacity, benchmark-scaled time predictions, and queue adaptationcapability. We present the basic motivations for all these featuresand discuss various issues regarding their implementations in the currentgrid environment.

  • 24.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, High Performance Compting Center North (HPC2N).
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A Grid Resource Broker Supporting Advance Reservations and Benchmark-based Resource Selection2006In: Applied Parallel Computing: State-of-the-art in Scientific Computing, Springer Verlag , 2006, p. 1061-1070Conference paper (Refereed)
    Abstract [en]

    This contribution presents algorithms, methods, and software for a Grid resource manager, responsible for resource brokering and scheduling in early production Grids. The broker selects computing resources based on actual job requirements and a number of criteria identifying the available resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, input/output data transfer, and executable staging. Main features of the resource manager include advance reservations, resource selection based on computer benchmark results and network performance predictions, and a basic adaptation facility.

  • 25.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A standards-based Grid resource brokering service supporting advance reservations, coallocation and cross-Grid interoperability2009In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 21, no 18, p. 2298-2335Article in journal (Refereed)
    Abstract [en]

    The problem of Grid-middleware interoperability is addressed by the design and analysis of a feature-rich, standards-based framework for all-to-all cross-middleware job submission.The architecture is designed with focus on generality and flexibility and builds on extensive use, internally and externally, of (proposed) Web and Grid services standards such asWSRF, JSDL, GLUE, and WS-Agreement. The external use providesthe foundation for easy integration into specific middlewares,which is performed by the design of a small set of plugins for each middleware. Currently, plugins are provided for integrationinto Globus Toolkit 4 and NorduGrid/ARC. The internal use of standard formats facilitates customizationof the job submission service by replacement of custom components for performing specific well-defined tasks.Most importantly, this enables the easy replacement of resource selection algorithms by algorithms that addresses the specific needs of a particular Grid environment and job submission scenario.By default, the service implements a decentralized brokering policy, strivingto optimize the performance for the individual user by minimizing the response time for each job submitted. The algorithms in our implementation perform resource selectionbased on performance predictions, and provide support for advance reservations as well as coallocation of multiple resources for coordinated use.The performance of the system is analyzed with focuson overall service throughput (up to over 250 jobs per minute)and individual job submission response time (down to under one second).

  • 26.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An Interoperable Standards-based Grid Resource Broker and Job Submission Service2005In: e-Science 2005: First IEEE Conference on e-Science and Grid Computing, IEEE Computer Society Press , 2005, p. 212-220Chapter in book (Other academic)
    Abstract [en]

    We present the architecture and implementation of a grid resource broker and job submission service, designed to be as independent as possible of the grid middleware used on the resources. The overall architecture comprises seven general components and a few conversion and integration points where all middleware-specific issues are handled. The implementation is based on state-of-the-art grid and Web services technology as well as existing and emerging standards (WSRF, JSDL, GLUE, WS-Agreement). Features provided by the service include advance reservations and a resource selection process based on a priori estimations of the total time to delivery for the application, including a benchmark-based prediction of the execution time. The general service implementation is based on the Globus Toolkit 4. For test and evaluation, plugins and format converters are provided for use with the NorduGrid ARC middleware

  • 27.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, High Performance Compting Center North (HPC2N).
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An Interoperable, Standards-Based Grid Resource Broker and Job Submission Service2005In: First International Conference on e-Science and Grid Computing, 2005, p. 212-220Conference paper (Refereed)
    Abstract [en]

    We present the architecture and implementation of a grid resource broker and job submission service, designed to be as independent as possible of the grid middleware used on the resources. The overall architecture comprises seven general components and a few conversion and integration points where all middleware-specific issues are handled. The implementation is based on state-of-the-art grid and Web services technology as well as existing and emerging standards (WSRF, JSDL, GLUE, WS-Agreement). Features provided by the service include advance reservations and a resource selection process based on a priori estimations of the total time to delivery for the application, including a benchmark-based prediction of the execution time. The general service implementation is based on the Globus Toolkit 4. For test and evaluation, plugins and format converters are provided for use with the NorduGrid ARC middleware

  • 28.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N). Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions2008In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 24, no 6, p. 585-593Article in journal (Refereed)
    Abstract [en]

    We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.

  • 29.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Self-management Challenges for Multi-cloud Architectures (Invited Paper)2011In: TOWARDS A SERVICE-BASED INTERNET, Berlin: Springer, 2011, Vol. 6994, p. 38-49Conference paper (Refereed)
    Abstract [en]

    Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages.

  • 30.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Self-management challenges for multi-cloud architectures2011In: Towards a Service-Based Internet: 4th European Conference, ServiceWave 2011, Poznan, Poland, October 26-28, 2011. Proceedings / [ed] Witold Abramowicz, Ignacio M. Llorente, Mike Surridge, Andrea Zisman and Julien Vayssière, Springer Berlin/Heidelberg, 2011, p. 38-49Conference paper (Refereed)
    Abstract [en]

    Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages.

  • 31.
    Espling, Daniel
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Armstrong, Django
    University of Leeds.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Djemame, Karim
    University of Leeds.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Contextualization: Dynamic Configuration of Virtual MachinesManuscript (preprint) (Other academic)
    Abstract [en]

    Virtual Machines (VMs) are commonly used as building blocks of IaaS cloud services. The number of running VM instances can be adjusted during runtime to achieve elasticity in the capacity of the service. New VM instances are based on templates that contain the basic configuration of the VM. Instance specific settings, settings unique to the infrastructure to which the instance is being deployed, are normally injected to the VM during the deployment phase through means of contextualization. In this work we present a layered approach to contextualization that supports different classes of contextualization data through the use of virtual devices. Inside each VM, data from different classes are layered on top of each other to create a unified file hierarchy using a small, custom file system. Context data can be updated during runtime by updating the contents of the virtual devices, making this approach the first contextualization approach to natively support recontextualization. Recontextualization enables run-time reconfiguration of a running service and can act as a trigger and key enabler of self-* techniques running inside the VM, allowing the service itself an unambiguous trigger for, e.g., further optimization in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are shown in an example with a distributed file system, demonstrating the feasibility of the approach.

  • 32.
    Espling, Daniel
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Larsson, Lars
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modeling and Placement of Cloud Services with Internal Structure2016In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 4, no 4, p. 429-439Article in journal (Refereed)
    Abstract [en]

    Virtual machine placement is the process of mapping virtual machines to available physical hosts within a datacenter or on a remote datacenter in a cloud federation. Normally, service owners cannot influence the placement of service components beyond choosing datacenter provider and deployment zone at that provider. For some services, however, this lack of influence is a hindrance to cloud adoption. For example, services that require specific geographical deployment (due e.g. to legislation), or require redundancy by avoiding co-location placement of critical components. We present an approach for service owners to influence placement of their service components by explicitly specifying service structure, component relationships, and placement constraints between components. We show how the structure and constraints can be expressed and subsequently formulated as constraints that can be used in placement of virtual machines in the cloud. We use an integer linear programming scheduling approach to illustrate the approach, show the corresponding mathematical formulation of the model, and evaluate it using a large set of simulated input. Our experimental evaluation confirms the feasibility of the model and shows how varying amounts of placement constraints and data center background load affects the possibility for a solver to find a solution satisfying all constraints within a certain time-frame. Our experiments indicate that the number of constraints affects the ability of finding a solution to a higher degree than background load, and that for a high number of hosts with low capacity, component affinity is the dominating factor affecting the possibility to find a solution.

  • 33. Ferrer, Ana Juan
    et al.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Aley-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Zsigri, Csilla
    Sirvent, Rauel
    Guitart, Jordi
    Badia, Rosa M.
    Djemame, Karim
    Ziegler, Wolfgang
    Dimitrakos, Theo
    Nair, Srijith K.
    Kousiouris, George
    Konstanteli, Kleopatra
    Varvarigou, Theodora
    Hudzia, Benoit
    Kipp, Alexander
    Wesner, Stefan
    Corrales, Marcelo
    Forgo, Nikolaus
    Sharif, Tabassum
    Sheridan, Craig
    OPTIMIS: A holistic approach to cloud service provisioning2012In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 28, no 1, p. 66-77Article in journal (Refereed)
    Abstract [en]

    We present fundamental challenges for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of cloud services. Our findings are incorporated in a toolkit targeting the cloud service and infrastructure providers. The innovations behind the toolkit are aimed at optimizing the whole service life cycle, including service construction, deployment, and operation, on a basis of aspects such as trust, risk, eco-efficiency and cost. Notably, adaptive self-preservation is crucial to meet predicted and unforeseen changes in resource requirements. By addressing the whole service life cycle, taking into account several cloud architectures, and by taking a holistic approach to sustainable service provisioning, the toolkit aims to provide a foundation for a reliable, sustainable, and trustful cloud computing industry.

  • 34.
    Kihl, Maria
    et al.
    Lund University.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Årzén, Karl-Erik
    Lund University.
    Robertsson, Anders
    Lund University.
    The Challenge of Cloud Control2013In: The 8th International Workshop on Feedback Computing (Feedback Computing '13), 2013Conference paper (Refereed)
    Abstract [en]

    Today’s cloud data center infrastructures are not even near being able to cope with the enormous and rapidly vary-ing capacity demands that will be reality in a near future. So far, very little is understood about how to transform today’s data centers (being large, power-hungry facilities, and operated through heroic efforts by numerous adminis-trators) into a self-managed, dynamic, and dependable infrastructure, constantly delivering expected QoS with rea-sonable operation costs and acceptable carbon footprint for large-scale services with sometimes dramatic variations in capacity demands. In this paper, we discuss some of the major challenges for resource-optimized cloud data cen-ter. We propose a new research area called Cloud Control, which is a control theoretic approach to a range of cloud management problems, aiming to transform today´s static and energy consuming cloud data centers into self-managed, dynamic, and dependable infrastructures, constantly delivering expected quality of service with acceptable operation costs and carbon footprint for large-scale services with varying capacity demands.

  • 35.
    Kostentinos Tesfatsion, Selome
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Proaño, Julio
    Tomás, Luis
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Caminero, Blanca
    Carrión, Carmen
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Power and Performance Optimization in FPGA-accelerated Clouds2018In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 30, no 18, article id e4526Article in journal (Other academic)
    Abstract [en]

    Energy management has become increasingly necessary in data centers to address all energy-related costs, including capital costs, operating expenses, and environmental impacts. Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field-Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy-aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data-intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance-energy ratio on a mix of multimedia and e-commerce applications.

  • 36.
    Kostentinos Tesfatsion, Selome
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Autonomic resource management for optimized power and performance in multi-tenant clouds2016In: 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC) / [ed] Samuel Kounev, Holger Giese, Jie Liu, LOS ALAMITOS: IEEE Computer Society, 2016, p. 85-94Conference paper (Refereed)
    Abstract [en]

    We present an autonomic resource management framework that takes advantage of both virtual machine resizing (CPU and memory) and physical CPU frequency scaling to reduce the power consumption of servers while meeting performance requirements of colocated applications. We design online performance and power model estimators that capture the complex relationships between applications' performance and server power (respectively), and resource utilization. Based on these models, we devise two optimization strategies to determine the most power efficient configuration. We also show that an operator can tune the tradeoff between power and performance. Our evaluation using a set of cloud benchmarks compares the proposed solution in power savings against the Linux ondemand and performance CPU governors. The results show that our solution achieves power savings between 12% to 20% compared to the baseline performance governor, while still meeting applications' performance goals.

  • 37.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A General Approach to Service Deployment in Cloud Environments2012In: Cloud and Green Computing (CGC 2012): 2012 Second International Conference on, IEEE Computer Society, 2012, p. 17-24Conference paper (Refereed)
    Abstract [en]

    The cloud computing landscape has recently developed into a spectrum of cloud architectures, leading to a broad range of management tools for similar operations but specialized for certain deployment scenarios. This both hinders the efficient reuse of algorithmic innovations within cloud management operations and increases the heterogeneity between different management systems. Our overarching goal is to overcome these problems by developing tools general enough to support the full range of popular architectures. In this contribution, we analyze commonalities in recently proposed cloud models (private clouds, multi-clouds, bursted clouds, federated clouds, etc.), and demonstrate how a key management functionality - service deployment - can be uniformly performed in all of these by a carefully designed system. The design of our service deployment framework is validated through a demonstration of how it can be used to deploy services, perform bursting and brokering, as well as mediate a cloud federation in the context of the OPTIMIS Toolkit.

  • 38.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes2013In: 6th IEEE/ACM International Conference on Utility and Cloud Computing, IEEE Computer Society, 2013, p. 187-194Conference paper (Refereed)
    Abstract [en]

    Until now, most research on cloud service placement has focused on static pricing scenarios, where cloud providers offer fixed prices for their resources. However, with the recent trend of dynamic pricing of cloud resources, where the price of a compute resource can vary depending on the free capacity and load of the provider, new placement algorithms are needed. In this paper, we investigate service placement in dynamic pricing scenarios by evaluating a set of placement algorithms, tuned for dynamic pricing. The algorithms range from simple heuristics to combinatorial optimization solutions. The studied algorithms are evaluated by deploying a set of services across multiple providers. Finally, we analyse the strengths and weaknesses of the algorithms considered. The evaluation suggests that exhaustive search based approach is good at finding optimal solutions for service placement under dynamic pricing schemes, but the execution times are usually long. In contrast, greedy approaches perform surprisingly well with fast execution times and acceptable solutions, and thus can be a suitable compromise considering the tradeoffs between quality of solution and execution time.

  • 39.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An aspect-oriented approach to consistency-preserving caching and compression of web service response messages2010In: Web Services (ICWS 2010): 2010 IEEE International Conference on, IEEE Computer Society, 2010, p. 526-533Conference paper (Refereed)
    Abstract [en]

    Web Services communicate through XMLencoded messages and suffer from substantial overhead due to verbose encoding of transferred messages and extensive (de)serialization at the end-points. We demonstrate that response caching is an effective approach to reduce Internet latency and server load. Our Tantivy middleware layer reduces the volume of data transmitted without semantic interpretation of service requests or responses and thus improves the service response time. Tantivy achieves this reduction through the combined use of caching of recent responses and data compression techniques to decrease the data representation size. These benefits do not compromise the strict consistency semantics. Tantivy also decreases the overhead of message parsing via storage of application-level data objects rather than XMLrepresentations. Furthermore, we demonstrate how the use of aspect-oriented programming techniques provides modularity and transparency in the implementation. Experimental evaluations based on the WSTest benchmark suite demonstrate that our Tantivy system gives significant performance improvements compared to non-caching techniques.

  • 40.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modeling for Dynamic Cloud Scheduling via Migration of Virtual Machines2011In: Cloud Computing Technology and Science (CloudCom), IEEE Computer Society, 2011, p. 163-171Conference paper (Refereed)
    Abstract [en]

    Cloud brokerage mechanisms are fundamental to reduce the complexity of using multiple cloud infrastructures to achieve optimal placement of virtual machines and avoid the potential vendor lock-in problems. However, current approaches are restricted to static scenarios, where changes in characteristics such as pricing schemes, virtual machine types, and service performance throughout the service life-cycle are ignored. In this paper, we investigate dynamic cloud scheduling use cases where these parameters are continuously changed, and propose a linear integer programming model for dynamic cloud scheduling. Our model can be applied in various scenarios through selections of corresponding objectives and constraints, and offers the flexibility to express different levels of migration overhead when restructuring an existing infrastructure. Finally, our approach is evaluated using commercial clouds parameters in selected simulations for the studied scenarios. Experimental results demonstrate that, with proper parametrizations, our approach is feasible.

  • 41.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Virtual machine placement for predictable and time-constrained peak loads2012In: Economics of Grids, Clouds, Systems, and Services: 8th International Workshop, GECON 2011, Paphos, Cyprus, December 5, 2011, Revised Selected Papers / [ed] Kurt Vanmechelen, Jörn Altmann, Omer F. Rana, Springer Berlin/Heidelberg, 2012, p. 120-134Conference paper (Refereed)
    Abstract [en]

    We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.

  • 42.
    Lorido-Botran, Tania
    et al.
    University of Deusto.
    Huerta, Sergio
    University of Deusto.
    Tomás, Luis
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sanz, Borja
    University of Deusto.
    An unsupervised approach to online noisy-neighbor detection in cloud data centers2017In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 89, p. 188-204Article in journal (Refereed)
    Abstract [en]

    Resource sharing is an inherent characteristic of cloud data centers. Virtual Machines (VMs) and/or Containers that are co-located in the same physical server often compete for resources leading to interference. The noisy neighbor’s effect refers to an anomaly caused by a VM/container limiting resources accessed by another one. Our main contribution is an online, lightweight and application-agnostic solution for anomaly detection, that follows an unsupervised approach. It is based on comparing models for different lags: Dirichlet Process Gaussian Mixture Models to characterize the resource usage profile of the application, and distance measures to score the similarity among models. An alarm is raised when there is an abrupt change in short-term lag (i.e. high distance score for short-term models), while the long-term state remains constant. We test the algorithm for different cloud workloads: websites, periodic batch applications, Spark-based applications, and Memcached server. We are able to detect anomalies in the CPU and memory resource usage with up to 82–96% accuracy (recall) depending on the scenario. Compared to other baseline methods, our approach is able to detect anomalies successfully, while raising low number of false positives, even in the case of applications with unusual normal behavior (e.g. periodic). Experiments show that our proposed algorithm is a lightweight and effective solution to detect noisy neighbor effect without any historical info about the application, that could also be potentially applied to other kind of anomalies.

  • 43.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds2018Report (Other academic)
    Abstract [en]

    Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoTs) applications in MECs. The scheduler at the higher level runs periodically – monitors system state and the performance of applications – and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application’s performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example.

    We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

  • 44.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds2018In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoT) applications in MECs. The scheduler at the higher level runs periodically - monitors system state and the performance of applications - and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application's performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example. We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

  • 45.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Durango, Jonas
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Online Spike Detection in Cloud Workloads2015In: 2015 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2015), New York: IEEE Computer Society, 2015, p. 446-451Conference paper (Refereed)
    Abstract [en]

    We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they falsely report spikes.

  • 46.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tärneberg, William
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kihl, Maria
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    How beneficial are intermediate layer Data Centers in Mobile Edge Networks?2016In: 2016 IEEE 1st International Workshops on Foundations and Applications of Self-* Systems / [ed] Sameh Elnikety, Peter R. Lewis and Christian Müller-Schloer, 2016, p. 222-229Conference paper (Refereed)
    Abstract [en]

    To reduce the congestion due to the future bandwidth-hungry applications in domains such as Health care, Internet of Things (IoT), etc., we study the benefit of introducing additional Data Centers (DCs) closer to the network edge for the optimal application placement. Our study shows that the edge layer DCs in a Mobile Edge Network (MEN) infrastructure is cost beneficial for the bandwidth-hungry applications having their strong demand locality and in the scenarios where large capacity is deployed at the edge layer DCs. The cost savings for such applications can go up to 67%. Additional intermediate layer DCs close to the root DC can be marginally cost beneficial for the compute intensive applications with medium or low demand locality. Hence, a Telecom Network Operator should start building an edge DC first having capacity up to hundreds of servers at the network edge to cater the emerging bandwidth-hungry applications and to minimize its operational cost.

  • 47. Nair, Srijith K.
    et al.
    Porwal, Sakshi
    Dimitrakos, Theo
    Ferrer, Ana Juan
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sharif, Tabassum
    Sheridan, Craig
    Rajarajan, Muttukrishnan
    Khan, Afnan Ullah
    Towards secure cloud bursting, brokerage and aggregation2010In: 2010 Eighth IEEE European Conference on Web Services, IEEE, 2010, p. 189-196Conference paper (Refereed)
    Abstract [en]

    The cloud based delivery model for IT resources is revolutionizing the IT industry. Despite the marketing hype around “the cloud”, the paradigm itself is in a critical transition state from the laboratories to mass market. Many technical and business aspects of cloud computing need to mature before it is widely adopted for corporate use. For example, the inability to seamlessly burst between internal cloud and external cloud platforms, termed cloud bursting, is a significant shortcoming of current cloud solutions. Furthermore, the absence of a capability that would allow to broker between multiple cloud providers or to aggregate them into a composite service inhibits the free and open competition that would help the market mature. This paper describes the concepts of cloud bursting and cloud brokerage and discusses the open management and security issues associated with the two models. It also presents a possible architectural framework capable of powering the brokerage based cloud services that is currently being developed in the scope of OPTIMIS, an EU FP7 project.

  • 48.
    Papadopoulos, Alessandro Vittorio
    et al.
    Lund University.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Årzén, Karl-Erik
    Lund University.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    PEAS: A Performance Evaluation framework for Auto-Scaling strategies in cloud applications2015Manuscript (preprint) (Other academic)
    Abstract [en]

    Numerous auto-scaling strategies have been proposed in the last few years for improving various Quality of Service (QoS)indicators of cloud applications, e.g., response time and throughput, by adapting the amount of resources assigned to theapplication to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved throughexperiments under specific conditions, and seldom includes extensive testing to account for uncertainties in the workloads, andunexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in generalconditions. In this paper, we present PEAS, a Performance Evaluation framework for Auto-Scaling strategies in the presenceof uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenariotheory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six differentauto-scaling strategies have been selected from the literature for extensive test evaluation, and compared using the proposedframework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics,highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of thealgorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show thatbased on the evaluation criteria, a controller can be shown to be better than other controllers.

  • 49. Proaño Orellana, Julio
    et al.
    Caminero, Bianca
    Carrión, Carmen
    Tomas, Luis
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kostentinos Tesfatsion, Selome
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    FPGA-Aware Scheduling Strategies at Hypervisor Level in Cloud Environments2016In: Scientific Programming, ISSN 1058-9244, E-ISSN 1875-919X, article id 4670271Article in journal (Refereed)
    Abstract [en]

    Current open issues regarding cloud computing include the support for nontrivial Quality of Service-related Service Level Objectives (SLOs) and reducing the energy footprint of data centers. One strategy that can contribute to both is the integration of accelerators as specialized resources within the cloud system. In particular, Field Programmable Gate Arrays (FPGAs) exhibit an excellent performance/energy consumption ratio that can be harnessed to achieve these goals. In this paper, a multilevel cloud scheduling framework is described, and several FPGA-aware node level scheduling strategies (applied at the hypervisor level) are explored and analyzed. These strategies are based on the use of a multiobjective metric aimed at providing Quality of Service (QoS) support. Results show how the proposed FPGA-aware scheduling policies increment the number of users requests serviced with their SLOs fulfilled while energy consumption is minimized. In particular, evaluation results of a use case based on a multimedia application show that the proposal can save more than 20% of the total energy compared with other baseline algorithms while a higher percentage of Service Level Agreement (SLA) is fulfilled.

  • 50. Rochwerger, B.
    et al.
    Breitgand, D.
    Epstein, A.
    Hadas, D.
    Loy, I.
    Nagin, K.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ragusa, C.
    Clayman, S.
    Levy, E.
    Maraschini, A.
    Massonet, P.
    Muñoz, H.
    Toffetti, G.
    Villari, M.
    Reservoir: When one cloud is not enough2011In: Computer, ISSN 0018-9162, E-ISSN 1558-0814, Vol. 44, no 3, p. 44-51Article in journal (Refereed)
    Abstract [en]

    As cloud computing becomes more predominant, the problem of scalability has become critical for cloud computing providers. The cloud paradigm is attractive because it offers a dramatic reduction in capital and operation expenses for consumers.

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