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Power Shepherd: Application Performance AwarePower Shifting
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)ORCID-id: 0000-0001-8178-3921
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. 2College of Information and Computer Sciences, University of Massachusetts Amherst.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Abstract [en]

Constantly growing power consumption of data centers is a major concern from environmental and economical reasons. Current approaches to reduce the negative consequences of high power consumption focus on limiting the peak power consumption. During the high workload periods, power consumption of highly utilized servers is throttled in order to stay within the power budget. However, the peak power reduction affects performance of hosted applications and thus leads to Quality of Service violations. In this paper, we introduce Power Shepherd, a hierarchical system for application performance aware power shifting. Power Shepherd reduces the data center operational costs by redistributing the available power among applications hosted in the cluster. This is achieved by, assigning server power budgets by the cluster controller, enforcing these power budgets using Running Average Power Limit (RAPL), and prioritizing applications within each server by adjusting the CPU scheduling configuration. We implement a prototype of the proposed solution and evaluate it in a real testbed equipped with power meters and using representative cloud applications. Our experiments show that Power Shepherd has potential to manage a cluster consisting of thousands of servers and limit the increase of operational costs by a significant amount when the cluster power budget is limited and the system is overutilized. Finally, we identify some outstanding challenges regarding model sensitivity and the fact that this approach in its current form is not beneficial to be used in all situations, e.g., when the system is underutilized.

Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:umu:diva-161362OAI: oai:DiVA.org:umu-161362DiVA, id: diva2:1334450
Tillgänglig från: 2019-07-02 Skapad: 2019-07-02 Senast uppdaterad: 2019-08-09
Ingår i avhandling
1. May the power be with you: managing power-performance tradeoffs in cloud data centers
Öppna denna publikation i ny flik eller fönster >>May the power be with you: managing power-performance tradeoffs in cloud data centers
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[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.

Ort, förlag, år, upplaga, sidor
Umeå University, 2019. s. 63
Serie
Report / UMINF, ISSN 0348-0542 ; 19.04
Nyckelord
cloud computing, data centers, power efficiency, power budgeting, application performance
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:umu:diva-161363 (URN)978-91-7855-080-7 (ISBN)
Disputation
2019-09-06, Aula Anatomica (Bio.A.206), Biologihuset, Umeå, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-08-15 Skapad: 2019-07-02 Senast uppdaterad: 2019-08-21Bibliografiskt granskad

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Krzywda, JakubAli-Eldin, AhmedWadbro, EddieÖstberg, Per-OlovElmroth, Erik

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