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Power and performance management of virtualized computing environments via lookahead control

Authors:

Jeffrey O. Kephart,

James E. Hanson,

Nagarajan Kandasamy,

Guofei JiangAuthors Info & Claims

Cluster Computing, Volume 12, Issue 1

Pages 1 - 15

https://doi.org/10.1007/s10586-008-0070-y

Published: 01 March 2009 Publication History

Abstract

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.

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Power and performance management of virtualized computing environments via lookahead control

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Published In

cover image Cluster Computing

Cluster Computing Volume 12, Issue 1

March 2009

96 pages

ISSN:1386-7857

Issue’s Table of Contents

Copyright © Copyright © 2009 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2009

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Chen ZLi JLi NJiao W(2024)Reliable proactive adaptation via prediction fusion and extended stochastic model predictive controlJournal of Systems and Software10.1016/j.jss.2024.112166217:COnline publication date: 1-Nov-2024
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Nakahira YFerragut AWierman A(2023)Generalized Exact SchedulingOperations Research10.1287/opre.2021.223271:2(433-470)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1287/opre.2021.2232
Saadi YJounaidi SEl Kafhali SZougagh H(2023)Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflowsComputing10.1007/s00607-023-01182-w105:10(2231-2261)Online publication date: 13-May-2023
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Kim MLee JRaza Shah SKim TNoh S(2021)Min-max exclusive virtual machine placement in cloud computing for scientific data environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-020-00221-710:1Online publication date: 11-Jan-2021
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Chaurasia NKumar MChaudhry RVerma O(2021)Comprehensive survey on energy-aware server consolidation techniques in cloud computingThe Journal of Supercomputing10.1007/s11227-021-03760-177:10(11682-11737)Online publication date: 1-Oct-2021
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Khaleel MZhu M(2021)Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centersThe Journal of Supercomputing10.1007/s11227-021-03753-077:10(11986-12025)Online publication date: 1-Oct-2021
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Caviglione LGaggero MPaolucci MRonco R(2021)Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacentersSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05462-x25:19(12569-12588)Online publication date: 1-Oct-2021
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