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  • 1.
    Camillo, Frédéric
    et al.
    University of Toulouse / ENSEEIHT.
    Caron, Eddy
    University of Lyon / École Normale Supérieure de Lyon.
    Guivarch, Ronan
    University of Toulouse / ENSEEIHT.
    Hurault, Aurélie
    University of Toulouse / ENSEEIHT.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Pérez, Christian
    University of Lyon / INRIA.
    Resource Management Architecture for Fair Scheduling of Optional Computations2013In: 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing: 3PGCIC 2013 / [ed] Fatos Xhafa, Leonard Barolli, Dritan Nace, Salvatore Vinticinque and Alain Bui, IEEE Computer Society, 2013, p. 113-120Conference paper (Refereed)
    Abstract [en]

    Most HPC platforms require users to submit a pre-determined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number of requests to submit for a Monte Carlo experiment is difficult to choose. The more requests are completed, the better the results are, however, submitting too many might overload the platform. Conversely, submitting too few requests may leave resources unused and misses an opportunity to improve the results.

    This paper introduces and solves the problem of scheduling optional computations. An architecture which auto-tunes the number of requests is proposed, then implemented in the DIET GridRPC middleware. Real-life experiments show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, the solution is shown to be scalable.

  • 2.
    Chaudhry, Tanmay
    et al.
    SimScale GmbH, Germany.
    Doblander, Christoph
    Technische Universität München, Germany.
    Dammer, Anatol
    SimScale GmbH, Germany.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Jacobsen, Hans-Arno
    Technische Universität München, Germany.
    Retrofitting Admission Control in an Internet-Scale Application2016Report (Other academic)
    Abstract [en]

    In this paper we propose a methodology to retrofit admission control in an Internet-scale, production application. Admission control requires less effort to improve the availability of an application, in particular when making it scalable is costly. This can occur due to the integration of 3rd-party legacy code or handling large amounts of data, and is further motivated by lean thinking, which argues for building a minimum viable product to discover customer requirements.

    Our main contribution consists in a method to generate an amplified workload, that is realistic enough to test all kinds of what-if scenarios, but does not require an exhaustive transition matrix. This workload generator can then be used to iteratively stress-test the application, identify the next bottleneck and add admission control.

    To illustrate the usefulness of the approach, we report on our experience with adding admission control within SimScale, a Software-as-a-Service start-up for engineering simulations, that already features 50,000 users.

  • 3. Chaudhry, Tanmay
    et al.
    Doblander, Christoph
    Dammer, Anatol
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Jacobsen, Hans-Arno
    Retrofitting Admission Control in an Internet-Scale Application2016Report (Other academic)
    Abstract [en]

    In this paper we propose a methodology to retrofit admission control in an Internet-scale, production application. Admission control requires less effort to improve the availability of an application, in particular when making it scalable is costly. This can occur due to the integration of 3rd-party legacy code or handling large amounts of data, and is further motivated by lean thinking, which argues for building a minimum viable product to discover customer requirements.

    Our main contribution consists in a method to generate an amplified workload, that is realistic enough to test all kinds of what-if scenarios, but does not require an exhaustive transition matrix. This workload generator can then be used to iteratively stress-test the application, identify the next bottleneck and add admission control.

    To illustrate the usefulness of the approach, we report on our experience with adding admission control within SimScale, a Software-as-a-Service start-up for engineering simulations, that already features 50,000 users.

  • 4. Durango, Jonas
    et al.
    Dellkrantz, Manfred
    Maggio, Martina
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Papadopoulos, Alessandro Vittorio
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Arzen, Karl-Erik
    Control-theoretical load-balancing for cloud applications with brownout2014In: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, p. 5320-5327Conference paper (Refereed)
    Abstract [en]

    Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas - copies of the applications having the same functionalities - for redundancy and adding a load-balancer to direct incoming traffic. However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing. In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a self-adaptive application simulator, together with some state-of-the-art solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

  • 5. Filieri, Antonio
    et al.
    Maggio, Martina
    Angelopoulos, Konstantinos
    D’ippolito, Nicolás
    Gerostathopoulos, Ilias
    Hempel, Andreas Berndt
    Hoffmann, Henry
    Jamshidi, Pooyan
    Kalyvianaki, Evangelia
    Klein, Cristian
    Krikava, Filip
    Misailovic, Sasa
    Papadopoulos, Alessandro Vittorio
    Lund University, Sweden.
    Ray, Suprio
    Sharifloo, Amir M.
    Shevtsov, Stepan
    Ujma, Mateusz
    Vogel, Thomas
    Control Strategies for Self-Adaptive Software Systems2017In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 11, no 4, article id 24Article in journal (Refereed)
    Abstract [en]

    The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.

    This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

  • 6.
    Klein, Cristian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Brownout: Building More Robust Cloud Applications2014In: 36th International Conference on Software Engineering (ICSE 2014), 2014, p. 700-711Conference paper (Refereed)
    Abstract [en]

    Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure level - for example hardware failures - and at the user workload level - flash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning. In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory. We modified two popular web application prototypes - RUBiS and RUBBoS - with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout self-adaptation dramatically improves the ability to withstand flash-crowds and hardware failures.

  • 7.
    Klein, Cristian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Introducing Service-level Awareness in the Cloud2013In: Proceedings of the 4th annual Symposium on Cloud Computing, Association for Computing Machinery (ACM), 2013Conference paper (Refereed)
  • 8.
    Klein, Cristian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Introducing Service-level Awareness in the Cloud2013Report (Other academic)
    Abstract [en]

    Resource allocation in clouds is mostly done assuming hard requirements, applications either receive the requested resources or fail. Given the dynamic nature of workloads, guaranteeing on-demand allocations requires large spare capacity. Hence, one cannot have a system that is both reliable and efficient.

    To solve this issue, we introduce Service-Level (SL) awareness in clouds, assuming applications contain some optional code that can be dynamically deactivated as needed. First, we design a model for such applications and synthesize a controller to decide when to execute the optional code and when to skip it. Then, we propose a Resource Manager (RM) that allocates resources to multiple SL-aware applications in a fair manner. We theoretically prove properties of the overall system using control and game theory.

    To show the practical applicability, we implemented SL-aware versions of RUBiS and RUBBoS with less than 170 lines of code. Experiments show that SL-awareness may enable a factor 8 improvement in withstanding flash-crowds or failures. SL-awareness opens up more flexibility in cloud resource management, which is why we encourage further research by publishing all source code.

  • 9.
    Klein, Cristian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Resource management for service level aware cloud applications2013Conference paper (Refereed)
    Abstract [en]

    Resource allocation in clouds is mostly done assuming hard requirements, time-sensitive applications either receive the requested resources or fail. Given the dynamic nature of workloads, guaranteeing on-demand allocations requires large spare capacity. Hence, one cannot have a system that is both reliable and efficient.

    To mitigate this issue, we introduce service-level awareness in clouds, assuming applications contain some optional code that can be dynamically deactivated as needed. We propose a resource manager that allocates resources to multiple service-level-aware applications in a fair manner. To show the practical applicability, we implemented service-level-aware versions of RUBiS and RUBBoS, two popular cloud benchmarks, together with our resource manager. Experiments show that service-level awareness helps in withstanding flash-crowds or failures, opening up more flexibility in cloud resource management.

  • 10.
    Klein, Cristian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Papadopoulos, Alessandro Vittorio
    Lund University, Sweden.
    Dellkrantz, Manfred
    Lund University, Sweden.
    Dürango, Jonas
    Lund University, Sweden.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Improving Cloud Service Resilience using Brownout-Aware Load-Balancing2014In: 2014 IEEE 33RD INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS (SRDS), IEEE Computer Society, 2014, p. 31-40Conference paper (Refereed)
    Abstract [en]

    We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a load-balancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading user experience (e.g., disabling recommendations).

    Combining resilience with the brownout paradigm is to date an open practical problem. The issue is to ensure that replica self-adaptivity would not confuse the load-balancing algorithm, overloading replicas that are already struggling with capacity shortage. For example, load-balancing strategies based on response times are not able to decide which replicas should be selected, since the response times are already controlled by the brownout paradigm.

    In this paper we propose two novel brownout-aware load-balancing algorithms. To test their practical applicability, we extended the popular lighttpd web server and load-balancer, thus obtaining a production-ready implementation. Experimental evaluation shows that the approach enables cloud services to remain responsive despite cascading failures. Moreover, when compared to Shortest Queue First (SQF), believed to be near-optimal in the non-adaptive case, our algorithms improve user experience by 5%, with high statistical significance, while preserving response time predictability.

  • 11. Kyriazis, Dimosthenis
    et al.
    Anagnostopoulos, Vasileios
    Arcangeli, Andrea
    Gilbert, David
    Kalogeras, Dimitrios
    Kat, Ronen
    Klein, Cristian
    Umeå University.
    Kokkinos, Panagiotis
    Kuperman, Yossi
    Nider, Joel
    Svärd, Petter
    Umeå University.
    Tomas, Luis
    Umeå University.
    Varvarigos, Emmanuel
    Varvarigou, Theodora
    High performance fault-tolerance for clouds2015In: 2015 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATION (ISCC), Larnaca, Cyprus, July 6-9, 2015, IEEE , 2015, p. 251-257Conference paper (Refereed)
    Abstract [en]

    Cloud computing and virtualized infrastructures are currently the baseline environments for the provision of services in different application domains. While the number of service consumers increasingly grows, service providers aim at exploiting infrastructures that enable non-disruptive service provisioning, thus minimizing or even eliminating downtime. Nonetheless, to achieve the latter current approaches are either application-specific or cost inefficient, requiring the use of dedicated hardware. In this paper we present the reference architecture of a fault-tolerance scheme, which not only enhances cloud environments with the aforementioned capabilities but also achieves high-performance as required by mission critical every day applications. To realize the proposed approach, a new paradigm for memory and I/O externalization and consolidation is introduced, while current implementation references are also provided.

  • 12.
    Lakew, Ewnetu B.
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Performance-Based Service Differentiation in Clouds2015In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), IEEE conference proceedings, 2015, p. 505-514Conference paper (Refereed)
    Abstract [en]

    Due to fierce competition, cloud providers need to run their data-centers efficiently. One of the issues is to increase data-center utilization while maintaining applications' performance targets. Achieving high data-center utilization while meeting applications' performance is difficult, as data-center overload may lead to poor performance of hosted services. Service differentiation has been proposed to control which services get degraded. However, current approaches are capacity-based, which are oblivious to the observed performance of each service and cannot divide the available capacity among hosted services so as to minimize overall performance degradation. In this paper we propose performance-based service differentiation. In case enough capacity is available, each service is automatically allocated the right amount of capacity that meets its target performance, expressed either as response time or throughput. In case of overload, we propose two service differentiation schemes that dynamically decide which services to degrade and to what extent. We carried out an extensive set of experiments using different services -- interactive as well as non-interactive -- by varying the workload mixes of each service over time. The results demonstrate that our solution precisely provides guaranteed performance or service differentiation depending on available capacity.

  • 13.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Cristian, Klein
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Francisco, Hernandez-Rodriguez
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Erik, Elmroth
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Towards faster response time models for vertical elasticity2014In: 2014 IEEE/ACM 7TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2014, p. 560-565Conference paper (Refereed)
    Abstract [en]

    Resource provisioning in cloud computing is typ- ically coarse-grained. For example, entire CPU cores may be allocated for periods of up to an hour. The Resource-as-a- Service cloud concept has been introduced to improve the efficiency of resource utilization in clouds. In this concept, resources are allocated in terms of CPU core fractions, with granularities of seconds. Such infrastructures could be created using existing technologies such as lightweight virtualization using LXC or by exploiting the Xen hypervisor’s capacity for vertical elasticity. However, performance models for de- termining how much capacity to allocate to each application are currently lacking. To address this deficit, we evaluate two performance models for predicting mean response times: the previously proposed queue length model and the novel inverse model. The models are evaluated using 3 applications under both open and closed system models. The inverse model reacted rapidly and remained stable even with targets as low as 0.5 seconds. 

  • 14.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Papadopoulos, Alessandro Vittorio
    Maggio, Martina
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    KPI-agnostic Control for Fine-Grained Vertical Elasticity2017In: 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), IEEE , 2017, p. 589-598Conference paper (Refereed)
    Abstract [en]

    Applications hosted in the cloud have become indispensable in several contexts, with their performance often being key to business operation and their running costs needing to be minimized. To minimize running costs, most modern virtualization technologies such as Linux Containers, Xen, and KVM offer powerful resource control primitives for individual provisioning - that enable adding or removing of fraction of cores and/or megabytes of memory for as short as few seconds. Despite the technology being ready, there is a lack of proper techniques for fine-grained resource allocation, because there is an inherent challenge in determining the correct composition of resources an application needs, with varying workload, to ensure deterministic performance.

    This paper presents a control-based approach for the management of multiple resources, accounting for the resource consumption, together with the application performance, enabling fine-grained vertical elasticity. The control strategy ensures that the application meets the target performance indicators, consuming as less resources as possible. We carried out an extensive set of experiments using different applications – interactive with response-time requirements, as well as non-interactive with throughput desires – by varying the workload mixes of each application over time. The results demonstrate that our solution precisely provides guaranteed performance while at the same time avoiding both resource over- and under-provisioning.

  • 15.
    Maggio, Martina
    et al.
    Lund University.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Årzén, Karl-Erik
    Lund University.
    Control strategies for predictable brownouts in cloud applications2014In: IFAC PAPERSONLINE, 2014, Vol. 47, p. 689-694Conference paper (Refereed)
    Abstract [en]

    Cloud computing is an application hosting model providing the illusion of infinite computing power. However, even the largest datacenters have finite computing capacity, thus cloud infrastructures have experienced overload due to overbooking or transient failures. The topic of this paper is the comparison of different control strategies to mitigate overload for datacenters, that assume that the running cloud applications are cooperative and help the infrastructure in recovering from critical events. Specifically, the paper investigates the behavior of different controllers when they have to keep the average response time of a cloud application below a certain threshold by acting on the probability of serving requests with optional computations disabled, where the pressure exerted by each request on the infrastructure is diminished, at the expense of user experience.

  • 16.
    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.

  • 17.
    Nguyen, Chanh Le Tan
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Location-aware load prediction in edge data centers2017In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2017, p. 25-31Conference paper (Other academic)
    Abstract [en]

    Mobile Edge Cloud (MEC) is a platform complementing traditional centralized clouds, consisting in moving computing and storage capacity closer to users -e. g., as Edge Data Centers (EDC) in base stations -in order to reduce application-level latency and network bandwidth. The bounded coverage radius of base station and limited capacity of each EDC intertwined with user mobility challenge the operator's ability to perform capacity adjustment and planning. To face this challenge, proactive resource provisioning can be performed. The resource usage in each EDC is estimated in advance, which is made available for the decision making to efficiently determine various management actions and ensure that EDCs persistently satisfies the Quality of Service (QoS), while maximizing resource utilization. In this paper, we propose location-aware load prediction. For each EDC, load is not only predicted using its own historical load time series -as done for centralized clouds -but also those of its neighbor EDCs. We employ Vector Autoregression Model (VAR) in which the correlation among adjacent EDCs load time series are exploited. We evaluate our approach using real world mobility traces to simulate load in each EDC and conduct various experiments to evaluate the proposed algorithm. Result shows that our proposed algorithm is able to achieve an average accuracy of up to 93% on EDCs with substantial average load, which slightly improves prediction by 4.3% compared to the state-of-the-art approach. Considering the expected scale of MEC, this translates to substantial cost savings e. g., servers can be shutdown without QoS violation.

  • 18.
    Papadopoulos, Alessandro Vittorio
    et al.
    Lund University, Sweden.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Maggio, Martina
    Lund University.
    Dürango, Jonas
    Lund University, Sweden.
    Dellkrantz, Manfred
    Lund University, Sweden.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Årzén, Karl-Erik
    Lund University.
    Control-based load-balancing techniques: Analysis and performance evaluation via a randomized optimization approach2016In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 52, p. 24-34Article in journal (Refereed)
    Abstract [en]

    Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas - copies of the application with the same functionality - for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer. Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing. To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF)-believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability.

  • 19.
    Shahrad, Mohammad
    et al.
    Princeton University.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Zheng, Liang
    Princeton University.
    Chiang, Mung
    Princeton University / Purdue University.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wentzlaf, David
    Princeton University.
    Incentivizing Self-Capping to Increase Cloud Utilization2017In: Proceedings of the 2017 Symposium on Cloud Computing (SOCC '17), Association for Computing Machinery (ACM), 2017, p. 52-65Conference paper (Refereed)
    Abstract [en]

    Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs. Higher utilization not only can enable higher profit for IaaS providers but also provides a mechanism to raise energy efficiency; therefore creating greener cloud services. Unfortunately, achieving high utilization is difficult mainly due to infrastructure providers needing to maintain spare capacity to service demand fluctuations.

    Graceful degradation is a self-adaptation technique originally designed for constructing robust services that survive resource shortages. Previous work has shown that graceful degradation can also be used to improve resource utilization in the cloud by absorbing demand fluctuations and reducing spare capacity. In this work, we build a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation. By using graceful degradation with an appropriate pricing model, the infrastructure provider can realize higher resource utilization while simultaneously, its tenants can increase their profit. Our proposed solution is based on a hybrid model which guarantees both reserved and peak on-demand capacities over flexible periods. It also includes a global dynamic price pair for capacity which remains uniform during each tenant's Service Level Agreement (SLA) term.

    We evaluate our scheme using simulations based on real-world traces and also implement a prototype using RUBiS on the Xen hypervisor as an end-to-end demonstration. Our analysis shows that the proposed scheme never hurts a tenant's net profit, but can improve it by as much as 93%. Simultaneously, it can also improve the effective utilization of contracts from 42% to as high as 99%.

  • 20.
    Tesfatsion, Selome Kostentinos
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    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.
    Virtualization Techniques Compared: Performance, Resource, and Power Usage Overheads in Clouds2018Manuscript (preprint) (Other academic)
    Abstract [en]

    Virtualization solutions based on hypervisors or containers are enabling technologies

    for scalable, flexible, and cost-effective resource sharing. As the fundamental

    limitations of each technology are yet to be understood, they need to be regularly

    reevaluated to better understand the trade-off provided by latest technological advances.

    This paper presents an in-depth quantitative analysis of virtualization

    overheads in these two groups of systems and their gaps relative to native environments

    based on a diverse set of workloads that stress CPU, memory, storage,

    and networking resources. KVM and XEN are used to represent hypervisor-based

    virtualization, and LXC and Docker for container-based platforms. The systems

    were evaluated with respect to several cloud resource management dimensions including

    performance, isolation, resource usage, energy efficiency, start-up time,

    and density. Our study is useful both to practitioners to understand the current

    state of the technology in order to make the right decision in the selection, operation

    and/or design of platforms and to scholars to illustrate how these technologies

    evolved over time.

  • 21.
    Tomás, Luis
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    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.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    The straw that broke the camel’s back: safe cloud overbooking with application brownout2014In: 2014 International Conference on Cloud and Autonomic Computing (ICCAC 2014), IEEE Press, 2014, p. 151-160Conference paper (Refereed)
    Abstract [en]

    Resource overbooking is an admission control technique to increase utilization in cloud environments. However, due to uncertainty about future application workloads, overbooking may result in overload situations and deteriorated performance. We mitigate this using brownout, a feedback approach to application performance steering, that ensures graceful degradation during load spikes and thus avoids overload. Additionally, brownout management information is included into the overbooking system, enabling the development of improved reactive methods to overload situations. Our combined brownout-overbooking approach is evaluated based on real-life interactive workloads and non-interactive batch applications. The results show that our approach achieves an improvement of resource utilization of 11 to 37 percentage points, while keeping response times lower than the set target of 1 second, with negligible application degradation.

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