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ALPACA: An Application Performance Aware Controller for Power Capping in Data Center Servers
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0001-8178-3921
Umeå University, Faculty of Science and Technology, Department of Computing Science. College of Information and Computer Sciences, University of Massachusetts Amherst. (Distributed Systems)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0003-4113-4788
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2018 (English)In: Proceedings of the 15th IEEE International Conference on Autonomic Computing (ICAC 2018), IEEE Computer Society, 2018, p. 41-50Conference paper, Published paper (Refereed)
Abstract [en]

Server power capping limits the power consumption of a server to not exceed a specific power budget. This allows data center operators to reduce the peak power consumption at the cost of performance degradation of hosted applications. Previous work on server power capping rarely considers Quality-of-Service (QoS) requirements of consolidated services when enforcing the power budget. In this paper, we introduce ALPACA, a framework to reduce QoS violations and overall application performance degradation for consolidated services. ALPACA reduces unnecessary high power consumption when there is no performance gain, and divides the power among the running services in a way that reduces the overall QoS degradation when the power is scarce. We evaluate ALPACA using four applications: MediaWiki, SysBench, Sock Shop, and CloudSuite’s Web Search benchmark. Our experiments show that ALPACA reduces the operational costs of QoS penalties and electricity by up to 40% compared to a non optimized system. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 41-50
Keywords [en]
power capping, performance degradation, power-performance tradeoffs
National Category
Computer Systems
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-132428DOI: 10.1109/ICAC.2018.00014ISBN: 978-1-5386-5139-1 (print)OAI: oai:DiVA.org:umu-132428DiVA, id: diva2:1081324
Conference
15th IEEE International Conference on Autonomic Computing (ICAC 2018)
Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2018-09-04
In thesis
1. Analysing, modelling and controlling power-performance tradeoffs in data center infrastructures
Open this publication in new window or tab >>Analysing, modelling and controlling power-performance tradeoffs in data center infrastructures
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Alternative title[sv]
Analys, modellering och reglering för avvägning mellan prestanda och strömförbrukning i datacenter
Abstract [en]

The aim of this thesis is to analyse the power-performance tradeoffs in datacenter servers, create models that capture these tradeoffs, and propose controllers to optimise the use of data center infrastructures taking the tradeoffs into consideration. The main research problem that we investigate in this thesis is how to increase the power efficiency of data center servers taking into account the power-performance tradeoffs.

The main cause for this research is the massive power consumption of data centers that is a concern both from the financial and environmental footprint perspectives. Irrespectively of the approaches taken to enhance data center power efficiency, substantial reductions in the power consumption of data center servers easily lead to performance degradation of hosted applications, which causes customers dissatisfaction. Therefore, it is crucial for the data center operators to understand and control the power-performance tradeoffs.

The research methods used in this thesis include experiments on real testbeds, applying statistical methods to create power-performance models, development of various optimisation techniques to improve the energy-efficiency of servers, and simulations to evaluate proposed solutions at scale.

As a result of the research presented in this thesis, we propose taxonomies for selected aspects of data center configurations, events, management actions, and monitored metrics. We discuss the relationships between these elements and to support the analysis present results from a set of testbed experiments.We show limitations in the applicability of various data center management actions, including Dynamic Voltage Frequency Scaling (DVFS), Running Average Power Limit (RAPL), CPU Pinning, horizontal and vertical scaling. Finally, we propose a power budgeting controller that minimizes the performance degradation while enforcing the power limits.

The outcomes of this thesis can be used by the data center operators to improve the energy-efficiency of servers and reduce the overall power consumption with minimized performance degradation. Moreover, the software artifacts including virtual machine images, scripts, and simulator are available online.

Future work includes further investigation of the problem of graceful performance degradation under power limits, incorporating multi-layer applications spread among several servers and load balancing controller.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2017
Series
Report / UMINF, ISSN 0348-0542 ; 17.04
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-132430 (URN)978-91-7601-683-1 (ISBN)
Presentation
2017-03-28, N360, Naturvetarhuset, Universitetsvägen, Umeå, 13:15 (English)
Supervisors
Available from: 2017-04-13 Created: 2017-03-13 Last updated: 2018-06-09Bibliographically approved

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Krzywda, Jakub

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