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Analysing, modelling and controlling power-performance tradeoffs in data center infrastructures
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
2017 (English)Licentiate thesis, comprehensive summary (Other academic)Alternative title
Analys, modellering och reglering för avvägning mellan prestanda och strömförbrukning i datacenter (Swedish)
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: urn:nbn:se:umu:diva-132430ISBN: 978-91-7601-683-1 (print)OAI: oai:DiVA.org:umu-132430DiVA, id: diva2:1081327
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
List of papers
1. A Sensor-Actuator Model for Data Center Optimization
Open this publication in new window or tab >>A Sensor-Actuator Model for Data Center Optimization
2015 (English)Report (Other academic)
Place, publisher, year, edition, pages
Sweden: Umeå University, 2015
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-132425 (URN)
Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2018-06-09
2. Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling
Open this publication in new window or tab >>Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling
Show others...
2018 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 81, p. 114-128Article in journal (Refereed) Published
Abstract [en]

Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, horizontal, and vertical scaling, are four techniques that have been proposed as actuators to control the performance and energy consumption on data center servers. This work investigates the utility of these four actuators, and quantifies the power-performance tradeoffs associated with them. Using replicas of the German Wikipedia running on our local testbed, we perform a set of experiments to quantify the influence of DVFS, vertical and horizontal scaling, and CPU pinning on end-to-end response time (average and tail), throughput, and power consumption with different workloads. Results of the experiments show that DVFS rarely reduces the power consumption of underloaded servers by more than 5%, but it can be used to limit the maximal power consumption of a saturated server by up to 20% (at a cost of performance degradation). CPU pinning reduces the power consumption of underloaded server (by up to 7%) at the cost of performance degradation, which can be limited by choosing an appropriate CPU pinning scheme. Horizontal and vertical scaling improves both the average and tail response time, but the improvement is not proportional to the amount of resources added. The load balancing strategy has a big impact on the tail response time of horizontally scaled applications.

Keywords
Power-performance tradeoffs, Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, Horizontal scaling, Vertical scaling
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-132427 (URN)10.1016/j.future.2017.10.044 (DOI)000423652200010 ()2-s2.0-85033772481 (Scopus ID)
Note

Originally published in thesis in manuscript form.

Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2018-06-09Bibliographically approved
3. ALPACA: An Application Performance Aware Controller for Power Capping in Data Center Servers
Open this publication in new window or tab >>ALPACA: An Application Performance Aware Controller for Power Capping in Data Center Servers
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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
Keywords
power capping, performance degradation, power-performance tradeoffs
National Category
Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-132428 (URN)10.1109/ICAC.2018.00014 (DOI)978-1-5386-5139-1 (ISBN)
Conference
15th IEEE International Conference on Autonomic Computing (ICAC 2018)
Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2018-09-04

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