research-article

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Fast and Accurate Load Balancing for Geo-Distributed Storage Systems

Authors:

Kirill L. Bogdanov,

Gerald Q. Maguire, Jr.,

Marco CaniniAuthors Info & Claims

SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

Pages 386 - 400

https://doi.org/10.1145/3267809.3267820

Published: 11 October 2018 Publication History

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%.

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cover image ACM Conferences

SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

October 2018

546 pages

ISBN:9781450360111

DOI:10.1145/3267809

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Published: 11 October 2018

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SoCC '18

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SoCC '18: ACM Symposium on Cloud Computing

October 11 - 13, 2018

CA, Carlsbad, USA

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Darius PK KMajumder PV N MSaha SJ SMitra P(2024)Multi-criteria Rank Based Geo-distributed Load Balancing for Cloud Computing2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493818(1-7)Online publication date: 22-Feb-2024
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Wang HShen HLi ZTian S(2021)GeoCol: A Geo-distributed Cloud Storage System with Low Cost and Latency using Reinforcement Learning2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS51616.2021.00023(149-159)Online publication date: Jul-2021
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