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Autonomic resource management for optimized power and performance in multi-tenant clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)Article in journal (Refereed) Submitted
Abstract [en]

We present an autonomic resource management framework that takes advantage of both virtual machine resizing (CPU and memory) and physical CPU frequency scaling to reduce the power consumption of servers while meeting performance requirements of colocated applications. We design online performance and power model estimators that capture the complex relationships between applications' performance and server power (respectively), and resource utilization. Based on these models, we devise two optimization strategies to determine the most power efficient configuration. We also show that an operator can tune the tradeoff beween power and performance. Our evaluation using a set of cloud benchmarks compares the proposed solution in power savings against the Linux ondemand and performance CPU governors. The results show that our solution achieves power savings between 12% to 20% compared to the baseline performance governor, while still meeting applications' performance goals.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-121089OAI: oai:DiVA.org:umu-121089DiVA: diva2:930985
Available from: 2016-05-26 Created: 2016-05-26 Last updated: 2016-05-26
In thesis
1. Energy-efficient resource provisioning for cloud data centers
Open this publication in new window or tab >>Energy-efficient resource provisioning for cloud data centers
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Energy efficiency has become a fundamental concern in data centers, raising issues to all energy-related costs, including capital costs, operating expenses, and environmental impact. Energy inefficiency is mainly caused by unoptimized use of energy by sub-components of these data centers. For example, energy can be lost due to transport and conversion, cooling, and lightning. Energy can be wasted while running an idle server or when using unoptimized functions to perform a task. Addressing this problem as a whole requires redesigning data centers, rethinking components, and implementing energy-aware algorithms for data center operation. As one step towards achieving this goal, this thesis focuses on the development of resource allocation algorithms to improve the energy efficiency of servers in virtualized data centers. The thesis proposes models, techniques, and algorithms to improve data center resource efficiency for optimized power and performance. We present approaches that takes advantage of horizontal scaling, vertical scaling, CPU frequency scaling, and the scheduling of FPGAs to reduce the power consumption of servers while meeting performance requirements of applications. We design online performance and power models to capture system behaviour while adapting to changes in the underlying infrastructure. Based on these models, we propose controllers that dynamically determine power-efficient resource allocations. We also devise optimization strategies for colocated applications and evaluate their suitability in a number of scenarios. The proposed strategies simplify the handling of trade-offs between power minimization and meeting performance targets. We also consider fluctuations in resource allocation in decision making. Additionally, we propose a scheduling algorithm for the use of custom hardware accelerators, FPGAs, and their integration to data centers for the purpose of increasing processing and energy efficiency. Our evaluation results demonstrate that our proposed approaches provide improved energy-efficient management of resources in virtualized data centers.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University, 2016. 24 p.
Series
, UMINF, ISSN 0348-0542 ; 16.05
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-121093 (URN)978-91-7601-433-2 (ISBN)
Supervisors
Available from: 2016-05-26 Created: 2016-05-26 Last updated: 2016-06-17Bibliographically approved

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