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Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)ORCID-id: 0000-0001-8178-3921
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Vise andre og tillknytning
2018 (engelsk)Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 81, s. 114-128Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2018. Vol. 81, s. 114-128
Emneord [en]
Power-performance tradeoffs, Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, Horizontal scaling, Vertical scaling
HSV kategori
Forskningsprogram
datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-132427DOI: 10.1016/j.future.2017.10.044ISI: 000423652200010Scopus ID: 2-s2.0-85033772481OAI: oai:DiVA.org:umu-132427DiVA, id: diva2:1081323
Merknad

Originally published in thesis in manuscript form.

Tilgjengelig fra: 2017-03-13 Laget: 2017-03-13 Sist oppdatert: 2019-07-02bibliografisk kontrollert
Inngår i avhandling
1. Analysing, modelling and controlling power-performance tradeoffs in data center infrastructures
Åpne denne publikasjonen i ny fane eller vindu >>Analysing, modelling and controlling power-performance tradeoffs in data center infrastructures
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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.

sted, utgiver, år, opplag, sider
Umeå: Umeå University, 2017
Serie
Report / UMINF, ISSN 0348-0542 ; 17.04
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-132430 (URN)978-91-7601-683-1 (ISBN)
Presentation
2017-03-28, N360, Naturvetarhuset, Universitetsvägen, Umeå, 13:15 (engelsk)
Veileder
Tilgjengelig fra: 2017-04-13 Laget: 2017-03-13 Sist oppdatert: 2018-06-09bibliografisk kontrollert
2. May the power be with you: managing power-performance tradeoffs in cloud data centers
Åpne denne publikasjonen i ny fane eller vindu >>May the power be with you: managing power-performance tradeoffs in cloud data centers
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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.

sted, utgiver, år, opplag, sider
Umeå University, 2019. s. 63
Serie
Report / UMINF, ISSN 0348-0542 ; 19.04
Emneord
cloud computing, data centers, power efficiency, power budgeting, application performance
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-161363 (URN)978-91-7855-080-7 (ISBN)
Disputas
2019-09-06, Aula Anatomica (Bio.A.206), Biologihuset, Umeå, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-08-15 Laget: 2019-07-02 Sist oppdatert: 2019-08-21bibliografisk kontrollert

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