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May the power be with you: managing power-performance tradeoffs in cloud data centers
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0001-8178-3921
2019 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Må kraften vara med dig : dynamisk avvägning mellan prestanda och strömförbrukning i datacenter (Swedish)
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 [en]
cloud computing, data centers, power efficiency, power budgeting, application performance
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-161363ISBN: 978-91-7855-080-7 (print)OAI: oai:DiVA.org:umu-161363DiVA, id: diva2:1334464
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
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: 2019-07-02
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
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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: 2019-07-02Bibliographically approved
3. ALPACA: Application Performance Aware Server Power Capping
Open this publication in new window or tab >>ALPACA: Application Performance Aware Server Power Capping
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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
Series
IEEE Conference Publication, ISSN 2474-0756
Keywords
power capping, performance degradation, power-performance tradeoffs
National Category
Computer Systems
Research subject
business data processing
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: 2019-08-07Bibliographically approved
4. Power-aware cloud brownout: Response time and power consumption control
Open this publication in new window or tab >>Power-aware cloud brownout: Response time and power consumption control
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
Keywords
Cloud computing, Degradation, Power demand, Servers, Time factors, Virtual machining
National Category
Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-144722 (URN)10.1109/CDC.2017.8264049 (DOI)978-1-5090-2873-3 (ISBN)978-1-5090-2872-6 (ISBN)978-1-5090-2874-0 (ISBN)
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
5. Power Shepherd: Application Performance AwarePower Shifting
Open this publication in new window or tab >>Power Shepherd: Application Performance AwarePower Shifting
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(English)Manuscript (preprint) (Other academic)
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.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-161362 (URN)
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-08-09
6. Modeling and Simulation of QoS-AwarePower Budgeting in Cloud Data Centers
Open this publication in new window or tab >>Modeling and Simulation of QoS-AwarePower Budgeting in Cloud Data Centers
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-161361 (URN)
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-08-09

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Krzywda, Jakub

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