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    Fast and Accurate Load Balancing for Geo-Distributed Storage Systems

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    p386-Bogdanov.pdf
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    Description:
    Open Access Conference Paper
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    Type
    Conference Paper
    Authors
    Bogdanov, Kirill L.
    Reda, Waleed
    Maguire, Gerald Q.
    Kostić, Dejan
    Canini, Marco cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-09-28
    Online Publication Date
    2018-09-28
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/630817
    
    Metadata
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    Abstract
    The increasing density of globally distributed datacenters reduces the network latency between neighboring datacenters and allows replicated services deployed across neighboring locations to share workload when necessary, without violating strict Service Level Objectives (SLOs). We present Kurma, a practical implementation of a fast and accurate load balancer for geo-distributed storage systems. At run-time, Kurma integrates network latency and service time distributions to accurately estimate the rate of SLO violations for requests redirected across geo-distributed datacenters. Using these estimates, Kurma solves a decentralized rate-based performance model enabling fast load balancing (in the order of seconds) while taming global SLO violations. We integrate Kurma with Cassandra, a popular storage system. Using real-world traces along with a geo-distributed deployment across Amazon EC2, we demonstrate Kurma’s ability to effectively share load among datacenters while reducing SLO violations by up to a factor of 3 in high load settings or reducing the cost of running the service by up to 17%.
    Citation
    Bogdanov, K. L., Reda, W., Maguire, G. Q., Kostić, D., & Canini, M. (2018). Fast and Accurate Load Balancing for Geo-Distributed Storage Systems. Proceedings of the ACM Symposium on Cloud Computing. doi:10.1145/3267809.3267820
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the ACM Symposium on Cloud Computing - SoCC '18
    ISBN
    9781450360111
    DOI
    10.1145/3267809.3267820
    ae974a485f413a2113503eed53cd6c53
    10.1145/3267809.3267820
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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