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  • 1. Kalyvianaki, Evangelia
    et al.
    Charalambous, Themistoklis
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hand, Steven
    Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters2014In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 9, no 2Article in journal (Refereed)
    Abstract [en]

    Resource management of virtualized servers in data centers has become a critical task, since it enables cost-effective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark website deployed on a prototype Xen-virtualized cluster.

  • 2.
    Rahimian, Fatemeh
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Computer System Lab. (CSL), SICS Swedish, Sweden.
    Payberah, Amir H.
    Computer System Lab. (CSL), SICS Swedish, Sweden.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Jelasity, Mark
    MTA SZTE Research Group on AI, Hungarian Academy of Sciences and University of Szeged, Hungary.
    Haridi, Seif
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    A Distributed Algorithm for Large-Scale Graph Partitioning2015In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 10, no 2, article id 12Article in journal (Refereed)
    Abstract [en]

    Balanced graph partitioning is an NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems, including the optimal storage of large sets of graph-structured data over several hosts. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable because they typically involve frequent global operations over the entire graph. In this article, we propose a fully distributed algorithm called JA-BE-JA that uses local search and simulated annealing techniques for two types of graph partitioning: edge-cut partitioning and vertex-cut partitioning. The algorithm is massively parallel: There is no central coordination, each vertex is processed independently, and only the direct neighbors of a vertex and a small subset of random vertices in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We show that the minimal edge-cut value empirically achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as Metis. In particular, on large social networks, JA-BE-JA outperforms Metis. We also show that JA-BE-JA computes very low vertex-cuts, which are proved significantly more effective than edge-cuts for processing most real-world graphs.

  • 3. Rzadca, K.
    et al.
    Datta, A.
    Kreitz, Gunnar
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Buchegger, Sonja
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Game-theoretic mechanisms to increase data availability in decentralized storage systems2015In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 10, no 3Article in journal (Refereed)
    Abstract [en]

    In a decentralized storage system, agents replicate each other’s data to increase availability. Compared to organizationally centralized solutions, such as cloud storage, a decentralized storage system requires less trust in the provider and may result in smaller monetary costs. Our system is based on reciprocal storage contracts that allow the agents to adopt to changes in their replication partners’ availability (by dropping inefficient contracts and forming new contracts with other partners). The data availability provided by the system is a function of the participating agents’ availability. However, a straightforward system in which agents’ matching is decentralized uses the given agent availability inefficiently. As agents are autonomous, the highly available agents form cliques replicating data between each other, which makes the system too hostile for the weakly available newcomers. In contrast, a centralized, equitable matching is not incentive compatible: it does not reward users for keeping their software running. We solve this dilemma by a mixed solution: an "adoption" mechanism in which highly available agents donate some replication space, which in turn is used to help the worst-off agents. We show that the adoption motivates agents to increase their availability (is incentive-compatible), but also that it is sufficient for acceptable data availability for weakly-available agents.

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