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Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters EVANGELIA KALYVIANAKI, City University London THEMISTOKLIS CHARALAMBOUS, Royal Institute of Technology STEVEN HAND, Microsoft Research Resource management of virtualized servers in data centers has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2626290
Publisher site
See Article on Publisher Site

Abstract

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters EVANGELIA KALYVIANAKI, City University London THEMISTOKLIS CHARALAMBOUS, Royal Institute of Technology STEVEN HAND, Microsoft Research Resource management of virtualized servers in data centers has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Jul 1, 2014

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