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ALPACA: Application Performance Aware Server Power Capping
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: ICAC 2018: 2018 IEEE International Conference on Autonomic Computing (ICAC), Trento, Italy, September 3-7, 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
Series
IEEE Conference Publication, ISSN 2474-0756
Keywords [en]
power capping, performance degradation, power-performance tradeoffs
National Category
Computer Systems
Research subject
business data processing
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: 2019-08-07Bibliographically approved
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
2. May the power be with you: managing power-performance tradeoffs in cloud data centers
Open this publication in new window or tab >>May the power be with you: managing power-performance tradeoffs in cloud data centers
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Må kraften vara med dig : dynamisk avvägning mellan prestanda och strömförbrukning i datacenter
Abstract [en]

The overall goal of the work presented in this thesis was to find ways of managing power-performance tradeoffs in cloud data centers. To this end, the relationships between the power consumption of data center servers and the performance of applications hosted in data centers are analyzed, models that capture these relationships are developed, and controllers to optimize the use of data center infrastructures are proposed.

The studies were motivated by the massive power consumption of modern data centers, which is a matter of significant financial and environmental concern. Various strategies for improving the power efficiency of data centers have been proposed, including server consolidation, server throttling, and power budgeting. However, no matter what strategy is used to enhance data center power efficiency, substantial reductions in the power consumption of data center servers can easily degrade the performance of hosted applications, causing customer dissatisfaction. It is therefore crucial for data center operators to understand and control power-performance tradeoffs.

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

This thesis makes multiple contributions. First, it introduces taxonomies for various aspects of data center configuration, events, management actions, and monitored metrics. We discuss the relationships between these elements and support our analysis with results from a set of testbed experiments. We demonstrate limitations on the usefulness of various data center management actions for controlling power consumption, including Dynamic Voltage Frequency Scaling (DVFS) and Running Average Power Limit (RAPL). We also demonstrate similar limitations on common measures for controlling application performance, including variation of operating system scheduling parameters, CPU pinning, and horizontal and vertical scaling. Finally, we propose a set of power budgeting controllers that act at the application, server, and cluster levels to minimize performance degradation while enforcing power limits.

The results and analysis presented in this thesis can be used by data center operators to improve the power-efficiency of servers and reduce overall operational costs while minimizing performance degradation. All of the software generated during this work, including controller source code, virtual machine images, scripts, and simulators, has been open-sourced.

Place, publisher, year, edition, pages
Umeå University, 2019. p. 63
Series
Report / UMINF, ISSN 0348-0542 ; 19.04
Keywords
cloud computing, data centers, power efficiency, power budgeting, application performance
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-161363 (URN)978-91-7855-080-7 (ISBN)
Public defence
2019-09-06, Aula Anatomica (Bio.A.206), Biologihuset, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2019-08-15 Created: 2019-07-02 Last updated: 2019-08-21Bibliographically approved

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Krzywda, JakubAli-Eldin, A.Wadbro, EddieÖstberg, Per-OlovElmroth, Erik

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