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  • 1.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Capacity Scaling for Elastic Compute Clouds2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    AbstractCloud computing is a computing model that allows better management, higher utiliza-tion and reduced operating costs for datacenters while providing on demand resourceprovisioning for different customers. Data centers are often enormous in size andcomplexity. In order to fully realize the cloud computing model, efficient cloud man-agement software systems that can deal with the datacenter size and complexity needto be designed and built.This thesis studies automated cloud elasticity management, one of the main andcrucial datacenter management capabilities. Elasticity can be defined as the abilityof cloud infrastructures to rapidly change the amount of resources allocated to anapplication in the cloud according to its demand. This work introduces algorithms,techniques and tools that a cloud provider can use to automate dynamic resource pro-visioning allowing the provider to better manage the datacenter resources. We designtwo automated elasticity algorithms for cloud infrastructures that predict the futureload for an application running on the cloud. It is assumed that a request is either ser-viced or dropped after one time unit, that all requests are homogeneous and that it takesone time unit to add or remove resources. We discuss the different design approachesfor elasticity controllers and evaluate our algorithms using real workload traces. Wecompare the performance of our algorithms with a state-of-the-art controller. We ex-tend on the design of the best performing controller out of our two controllers anddrop the assumptions made during the first design. The controller is evaluated with aset of different real workloads.All controllers are designed using certain assumptions on the underlying systemmodel and operating conditions. This limits a controller’s performance if the modelor operating conditions change. With this as a starting point, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components,an analyzer and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

  • 2.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    El-Ansary, Sameh
    Nile University.
    Optimizing Replica Placement in Peer-Assisted Cloud Stores2011Conference paper (Refereed)
    Abstract [en]

    Peer-assisted cloud storage systems use the unutilizedresources of the clients subscribed to a storage cloudto offload the servers of the cloud. The provider distributesdata replicas on the clients instead of replicating on the localinfrastructure. These replicas allow the provider to providea highly available, reliable and cheap service at a reducedcost. In this work we introduce NileStore, a protocol forreplication management in peer-assisted cloud storage. Theprotocol converts the replica placement problem into a lineartask assignment problem. We design five utility functionsto optimize placement taking into account the bandwidth,free storage and the size of data in need of replication oneach peer. The problem is solved using a suboptimal greedyoptimization algorithm. We show our simulation results usingthe different utilities under realistic network conditions. Ourresults show that using our approach offloads the cloud serversby about 90% compared to a random placement algorithmwhile consuming 98.5% less resources compared to a normalstorage cloud.

  • 3.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    El-Ansary, Sameh
    Nile University.
    Replica Placement in Peer-Assisted Clouds: An Economic Approach2011In: Lecture Notes in Computer Science / [ed] Pascal Felber, Romain Rouvoy, Springer, 2011, p. 208-213Conference paper (Refereed)
    Abstract [en]

    We introduce NileStore, a replica placement algorithm based on an economical model for use in Peer-assisted cloud storage. The algorithm uses storage and bandwidth resources of peers to offload the cloud provider’s resources. We formulate the placement problem as a linear task assignment problem where the aim is to minimize time needed for file replicas to reach a certain desired threshold. Using simulation, We reduce the probability of a file being served from the provider’s servers by more than 97.5% under realistic network conditions.

  • 4.
    Ali-Eldin, Ahmed
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ilyushkin, Alexey
    Ghit, Bogdan
    Herbst, Nikolas Roman
    Papadopoulos, Alessandro
    Losup, Alexandru
    Which Cloud Auto-Scaler Should I Use for my Application?: Benchmarking Auto-Scaling Algorithms2016In: PROCEEDINGS OF THE 2016 ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING (ICPE'16), Association for Computing Machinery (ACM), 2016, p. 131-132Conference paper (Refereed)
  • 5.
    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.

  • 6.
    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.

  • 7.
    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%.

  • 8.
    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.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    Ali-Eldin Hassan, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Workload characterization, controller design and performance evaluation for cloud capacity autoscaling2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components;an analyzer, and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads, and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

  • 13. Bauer, André
    et al.
    Herbst, Nikolas
    Spinner, Simon
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science. UMass, Amherst, MA, USA.
    Kounev, Samuel
    Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field2019In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183, Vol. 30, no 4, p. 800-813Article in journal (Refereed)
    Abstract [en]

    Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.

  • 14.
    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.))
  • 15.
    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.

  • 16.
    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.

  • 17. 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.

  • 18. Ilyushkin, Alexey
    et al.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science. UMass, Amherst.
    Herbst, Nikolas
    Bauer, Andre
    Papadopoulos, Alessandro V.
    Epema, Dick
    Iosup, Alexandru
    An Experimental Performance Evaluation of Autoscalers for Complex Workflows2018In: ACM Transactions on Modeling and Performance Evaluation of Computing Systems, ISSN 2376-3639, Vol. 3, no 2, article id 8Article in journal (Refereed)
    Abstract [en]

    Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined quality of service target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy, as there is seldom enough analysis on the performance of the autoscalers in different operating conditions and with different applications. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a popular formalism for automating resource management for applications with well-defined yet complex structures. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the seven policies, we conduct various experiments and compare their performance in both pairwise and group comparisons. We report both individual and aggregated metrics. As many workflows have deadline requirements on the tasks, we study the effect of autoscaling on workflow deadlines. Additionally, we look into the effect of autoscaling on the accounted and hourly based charged costs, and we evaluate performance variability caused by the autoscaler selection for each group of workflow sizes. Our results highlight the trade-offs between the suggested policies, how they can impact meeting the deadlines, and how they perform in different operating conditions, thus enabling a better understanding of the current state-of-the-art.

  • 19.
    Krzywda, Jakub
    et al.
    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. College of Information and Computer Sciences, University of Massachusetts Amherst.
    Wadbro, Eddie
    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.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    ALPACA: Application Performance Aware Server Power Capping2018In: ICAC 2018: 2018 IEEE International Conference on Autonomic Computing (ICAC), Trento, Italy, September 3-7, 2018, IEEE Computer Society, 2018, p. 41-50Conference paper (Refereed)
    Abstract [en]

    Server power capping limits the power consumption of a server to not exceed a specific power budget. This allows data center operators to reduce the peak power consumption at the cost of performance degradation of hosted applications. Previous work on server power capping rarely considers Quality-of-Service (QoS) requirements of consolidated services when enforcing the power budget. In this paper, we introduce ALPACA, a framework to reduce QoS violations and overall application performance degradation for consolidated services. ALPACA reduces unnecessary high power consumption when there is no performance gain, and divides the power among the running services in a way that reduces the overall QoS degradation when the power is scarce. We evaluate ALPACA using four applications: MediaWiki, SysBench, Sock Shop, and CloudSuite’s Web Search benchmark. Our experiments show that ALPACA reduces the operational costs of QoS penalties and electricity by up to 40% compared to a non optimized system. 

  • 20.
    Krzywda, Jakub
    et al.
    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.
    Carlson, Trevor E.
    Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden.
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling2018In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 81, p. 114-128Article in journal (Refereed)
    Abstract [en]

    Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, horizontal, and vertical scaling, are four techniques that have been proposed as actuators to control the performance and energy consumption on data center servers. This work investigates the utility of these four actuators, and quantifies the power-performance tradeoffs associated with them. Using replicas of the German Wikipedia running on our local testbed, we perform a set of experiments to quantify the influence of DVFS, vertical and horizontal scaling, and CPU pinning on end-to-end response time (average and tail), throughput, and power consumption with different workloads. Results of the experiments show that DVFS rarely reduces the power consumption of underloaded servers by more than 5%, but it can be used to limit the maximal power consumption of a saturated server by up to 20% (at a cost of performance degradation). CPU pinning reduces the power consumption of underloaded server (by up to 7%) at the cost of performance degradation, which can be limited by choosing an appropriate CPU pinning scheme. Horizontal and vertical scaling improves both the average and tail response time, but the improvement is not proportional to the amount of resources added. The load balancing strategy has a big impact on the tail response time of horizontally scaled applications.

  • 21.
    Krzywda, Jakub
    et al.
    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. 2College of Information and Computer Sciences, University of Massachusetts Amherst.
    Wadbro, Eddie
    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.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Power Shepherd: Application Performance AwarePower ShiftingManuscript (preprint) (Other academic)
    Abstract [en]

    Constantly growing power consumption of data centers is a major concern from environmental and economical reasons. Current approaches to reduce the negative consequences of high power consumption focus on limiting the peak power consumption. During the high workload periods, power consumption of highly utilized servers is throttled in order to stay within the power budget. However, the peak power reduction affects performance of hosted applications and thus leads to Quality of Service violations. In this paper, we introduce Power Shepherd, a hierarchical system for application performance aware power shifting. Power Shepherd reduces the data center operational costs by redistributing the available power among applications hosted in the cluster. This is achieved by, assigning server power budgets by the cluster controller, enforcing these power budgets using Running Average Power Limit (RAPL), and prioritizing applications within each server by adjusting the CPU scheduling configuration. We implement a prototype of the proposed solution and evaluate it in a real testbed equipped with power meters and using representative cloud applications. Our experiments show that Power Shepherd has potential to manage a cluster consisting of thousands of servers and limit the increase of operational costs by a significant amount when the cluster power budget is limited and the system is overutilized. Finally, we identify some outstanding challenges regarding model sensitivity and the fact that this approach in its current form is not beneficial to be used in all situations, e.g., when the system is underutilized.

  • 22.
    Krzywda, Jakub
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Meyer, Vinicius
    Xavier, Miguel G.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science. College of Information and Computer Sciences, University of Massachusetts Amherst.
    Östberg, Per-Olov
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    De Rose, Cesar A. F.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modeling and Simulation of QoS-AwarePower Budgeting in Cloud Data CentersManuscript (preprint) (Other academic)
    Abstract [en]

    Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

  • 23.
    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.

  • 24.
    Östberg, Per-Olov
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Groenda, Henning
    Wesner, Stefan
    Byrne, James
    Nikolopoulos, Dimitris S.
    Sheridan, Craig
    Krzywda, Jakub
    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.
    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.
    Stier, Christian
    Krogmann, Klaus
    Domaschka, Jörg
    Hauser, Christopher B.
    Byrne, PJ
    Svorobej, Sergej
    McCollum, Barry
    Papazachos, Zafeiros
    Whigham, Darren
    Rüth, Stefan
    Paurevic, Dragana
    The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation2014In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014, p. 26-31Conference paper (Refereed)
    Abstract [en]

    Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.

1 - 24 of 24
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