CLARINET: WAN-Aware Optimization for Analytics Queries


Raajay Viswanathan, University of Wisconsin—Madison; Ganesh Ananthanarayanan, Microsoft; Aditya Akella, University of Wisconsin—Madison


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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@inproceedings {199374,
author = {Raajay Viswanathan and Ganesh Ananthanarayanan and Aditya Akella},
title = {{CLARINET}: {WAN-Aware} Optimization for Analytics Queries},
booktitle = {12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)},
year = {2016},
isbn = {978-1-931971-33-1},
address = {Savannah, GA},
pages = {435--450},
url = {},
publisher = {USENIX Association},
month = nov,

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