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Optimal Service Elasticity in Large-Scale Distributed Systems

Optimal Service Elasticity in Large-Scale Distributed Systems Optimal Service Elasticity in Large-Scale Distributed Systems DEBANKUR MUKHERJEE, Eindhoven University of Technology SOUVIK DHARA, Eindhoven University of Technology SEM C. BORST , Eindhoven University of Technology JOHAN S.H. VAN LEEUWAARDEN, Eindhoven University of Technology A fundamental challenge in large-scale cloud networks and data centers is to achieve highly efficient server utilization and limit energy consumption, while providing excellent user-perceived performance in the presence of uncertain and time-varying demand patterns. Auto-scaling provides a popular paradigm for automatically adjusting service capacity in response to demand while meeting performance targets, and queue-driven autoscaling techniques have been widely investigated in the literature. In typical data center architectures and cloud environments however, no centralized queue is maintained, and load balancing algorithms immediately distribute incoming tasks among parallel queues. In these distributed settings with vast numbers of servers, centralized queue-driven auto-scaling techniques involve a substantial communication overhead and major implementation burden, or may not even be viable at all. Motivated by the above issues, we propose a joint auto-scaling and load balancing scheme which does not require any global queue length information or explicit knowledge of system parameters, and yet provides provably near-optimal service elasticity. We establish the fluid-level dynamics for the proposed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the ACM on Measurement and Analysis of Computing Systems Association for Computing Machinery

Optimal Service Elasticity in Large-Scale Distributed Systems

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2476-1249
DOI
10.1145/3084463
Publisher site
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Abstract

Optimal Service Elasticity in Large-Scale Distributed Systems DEBANKUR MUKHERJEE, Eindhoven University of Technology SOUVIK DHARA, Eindhoven University of Technology SEM C. BORST , Eindhoven University of Technology JOHAN S.H. VAN LEEUWAARDEN, Eindhoven University of Technology A fundamental challenge in large-scale cloud networks and data centers is to achieve highly efficient server utilization and limit energy consumption, while providing excellent user-perceived performance in the presence of uncertain and time-varying demand patterns. Auto-scaling provides a popular paradigm for automatically adjusting service capacity in response to demand while meeting performance targets, and queue-driven autoscaling techniques have been widely investigated in the literature. In typical data center architectures and cloud environments however, no centralized queue is maintained, and load balancing algorithms immediately distribute incoming tasks among parallel queues. In these distributed settings with vast numbers of servers, centralized queue-driven auto-scaling techniques involve a substantial communication overhead and major implementation burden, or may not even be viable at all. Motivated by the above issues, we propose a joint auto-scaling and load balancing scheme which does not require any global queue length information or explicit knowledge of system parameters, and yet provides provably near-optimal service elasticity. We establish the fluid-level dynamics for the proposed

Journal

Proceedings of the ACM on Measurement and Analysis of Computing Systems – Association for Computing Machinery

Published: Jun 13, 2017

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