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OptiBook: Optimal Resource Booking for Energy-efficient Datacenters
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud computing)
Red Hat, Madrid, Spain.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud computing)
2017 (English)In: 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), IEEE Communications Society, 2017Conference paper, Published paper (Refereed)
Abstract [en]

A lack of energy proportionality, low resource utilization, and interference in virtualized infrastructure make the cloud a challenging target environment for improving energy efficiency. In this paper we present OptiBook, a system that improves energy proportionality and/or resource utilization to optimize performance and energy efficiency. OptiBook shares servers between latency-sensitive services and batch jobs, over- books the system in a controllable manner, uses vertical (CPU and DVFS) scaling for prioritized virtual machines, and applies performance isolation techniques such as CPU pinning and quota enforcement as well as online resource tuning to effectively improve energy efficiency. Our evaluations show that on average, OptiBook improves performance per watt by 20% and reduces energy consumption by 9% while minimizing SLO violations. 

Place, publisher, year, edition, pages
IEEE Communications Society, 2017.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-145492DOI: 10.1109/IWQoS.2017.7969135ISI: 000428199300029ISBN: 978-1-5386-2704-4 (electronic)ISBN: 978-1-5386-2705-1 (print)OAI: oai:DiVA.org:umu-145492DiVA, id: diva2:1188260
Conference
2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), Vilanova i la Geltrú, Spain, June 14-16, 2017
Available from: 2018-03-07 Created: 2018-03-07 Last updated: 2018-06-09Bibliographically approved
In thesis
1. Energy-efficient cloud computing: autonomic resource provisioning for datacenters
Open this publication in new window or tab >>Energy-efficient cloud computing: autonomic resource provisioning for datacenters
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Energy efficiency has become an increasingly important concern in data centers because of issues associated with energy consumption, such as capital costs, operating expenses, and environmental impact. While energy loss due to suboptimal use of facilities and non-IT equipment has largely been reduced through the use of best-practice technologies, addressing energy wastage in IT equipment still requires the design and implementation of energy-aware resource management systems. This thesis focuses on the development of resource allocation methods to improve energy efficiency in data centers. The thesis employs three approaches to improve efficiency for optimized power and performance: scaling virtual machine (VM) and server processing capabilities to reduce energy consumption; improving resource usage through workload consolidation; and exploiting resource heterogeneity.

To achieve these goals, the first part of the thesis proposes models, algorithms, and techniques that reduce energy usage through the use of VM scaling, VM sizing for CPU and memory, CPU frequency adaptation, as well as hardware power capping for server-level resource allocation. The proposed online performance and power models capture system behavior while adapting to changes in the underlying infrastructure. Based on these models, the thesis proposes controllers that dynamically determine power-efficient resource allocations while minimizing performance penalty.

These methods are then extended to support resource overbooking and workload consolidation to improve resource utilization and energy efficiency across the cluster or data center. In order to cater for different performance requirements among collocated applications, such as latency-sensitive services and batch jobs, the controllers apply service differentiation among prioritized VMs and performance isolation techniques, including CPU pinning, quota enforcement, and online resource tuning.

This thesis also considers resource heterogeneity and proposes heterogeneousaware scheduling techniques to improve energy efficiency by integrating hardware accelerators (in this case FPGAs) and exploiting differences in energy footprint of different servers. In addition, the thesis provides a comprehensive study of the overheads associated with a number of virtualization platforms in order to understand the trade-offs provided by the latest technological advances and to make the best resource allocation decisions accordingly. The proposed methods in this thesis are evaluated by implementing prototypes on real testbeds and conducting experiments using real workload data taken from production systems and synthetic workload data that we generated. Our evaluation results demonstrate that the proposed approaches provide improved energy management of resources in virtualized data centers.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 63
Series
Report / UMINF, ISSN 0348-0542 ; 18.05
Keywords
Cloud computing, datacenter, energy-efficiency, performance management, virtualization
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-145926 (URN)978-91-7601-862-0 (ISBN)
Public defence
2018-04-16, MA121, MIT-building, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2018-03-26 Created: 2018-03-22 Last updated: 2018-06-09Bibliographically approved

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Tesfatsion, Selome KostentinosTomás, LuisTordsson, Johan

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