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CLARINET: WAN-aware optimization for analytics queries

Published: 02 November 2016 Publication History
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  • Abstract

    Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of response time is the query execution plan (QEP). Current query optimizers do not consider the network when deriving QEPs, which is a key drawback as the geo-distributed sites are connected via WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks. We propose CLARINET, a novel WAN-aware query optimizer. Deriving a WAN-aware QEP requires working jointly with the execution layer of analytics frameworks that places tasks to sites and performs scheduling. We design efficient heuristic solutions in CLARINET to make such a joint decision on the QEP. Our experiments with a real prototype deployed across EC2 datacenters, and large-scale simulations using production workloads show that CLARINET improves query response times by ≥ 50% compared to state-of-the-art WAN-aware task placement and scheduling.

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    OSDI'16: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation
    November 2016
    786 pages
    ISBN:9781931971331

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    Published: 02 November 2016

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