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  • 1.
    Klein, Cristian
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Brownout: Building More Robust Cloud Applications2014Ingår i: 36th International Conference on Software Engineering (ICSE 2014), ACM Digital Library, 2014, s. 700-711Konferensbidrag (Refereegranskat)
    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.

  • 2.
    Klein, Cristian
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Introducing Service-level Awareness in the Cloud2013Ingår i: Proceedings of the 4th annual Symposium on Cloud Computing, Association for Computing Machinery (ACM), 2013Konferensbidrag (Refereegranskat)
  • 3.
    Klein, Cristian
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Introducing Service-level Awareness in the Cloud2013Rapport (Övrigt vetenskapligt)
    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.

  • 4.
    Klein, Cristian
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Maggio, Martina
    Lund University.
    Årzén, Karl-Erik
    Lund University.
    Hernández-Rodriguez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Resource management for service level aware cloud applications2013Konferensbidrag (Refereegranskat)
    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.

  • 5.
    Klein, Cristian
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    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 universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Improving Cloud Service Resilience using Brownout-Aware Load-Balancing2014Ingår i: 2014 IEEE 33RD INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS (SRDS), IEEE Computer Society, 2014, s. 31-40Konferensbidrag (Refereegranskat)
    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.

  • 6.
    Maggio, Martina
    et al.
    Lund University.
    Klein, Cristian
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Årzén, Karl-Erik
    Lund University.
    Control strategies for predictable brownouts in cloud applications2014Ingår i: IFAC PAPERSONLINE, 2014, Vol. 47, s. 689-694Konferensbidrag (Refereegranskat)
    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.

  • 7.
    Papadopoulos, Alessandro Vittorio
    et al.
    Lund University, Sweden.
    Klein, Cristian
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Maggio, Martina
    Lund University.
    Dürango, Jonas
    Lund University, Sweden.
    Dellkrantz, Manfred
    Lund University, Sweden.
    Hernández-Rodriguez, Francisco
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Årzén, Karl-Erik
    Lund University.
    Control-based load-balancing techniques: Analysis and performance evaluation via a randomized optimization approach2016Ingår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 52, s. 24-34Artikel i tidskrift (Refereegranskat)
    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.

1 - 7 av 7
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