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Power-aware cloud brownout: Response time and power consumption control
IDT, Mälardalen University, Sweden.
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. (Distributed Systems)
Department of Automatic Control, Lund University, Sweden.
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), IEEE, 2017, p. 2686-2691Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy capacity offered by the data center infrastructure. In this paper we combine an established technique for response time control — brownout — with power capping. We use cascaded control to take into account both the need for predictability in the response times (the inner loop), and the power cap (the outer loop). We execute tests on real machines to determine power usage and response times models and extend an existing simulator. We then evaluate the cascaded controller approach with a variety of workloads and both open- and closed-loop client models.

Place, publisher, year, edition, pages
IEEE, 2017. p. 2686-2691
Keywords [en]
Cloud computing, Degradation, Power demand, Servers, Time factors, Virtual machining
National Category
Computer Systems
Research subject
business data processing
Identifiers
URN: urn:nbn:se:umu:diva-144722DOI: 10.1109/CDC.2017.8264049ISBN: 978-1-5090-2873-3 (electronic)ISBN: 978-1-5090-2872-6 (electronic)ISBN: 978-1-5090-2874-0 (print)OAI: oai:DiVA.org:umu-144722DiVA, id: diva2:1182083
Conference
2017 IEEE 56th Annual Conference on Decision and Control (CDC), December 12-15, 2017, Melbourne, Australia
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2019-07-02Bibliographically approved
In thesis
1. 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, JakubElmroth, Erik

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