umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
PerfGreen: Performance and Energy Aware Resource Provisioning for Heterogeneous Clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud computing)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud computing)
2018 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Improving energy efficiency in a cloud environment is challenging because of

poor energy proportionality, low resource utilization, interference as well as workload,

application, and hardware dynamism. In this paper we present PerfGreen,

a dynamic auto-tuning resource management system for improving energy efficiency

with minimal performance impact in heterogeneous clouds. PerfGreen

achieves this through a combination of admission control, scheduling, and online

resource allocation methods with performance isolation and application priority

techniques. Scheduling in PerfGreen is energy aware and power management capabilities

such as CPU frequency adaptation and hard CPU power limiting are

exploited. CPU scaling is combined with performance isolation techniques, including

CPU pinning and quota enforcement, for prioritized virtual machines to

improve energy efficiency. An evaluation based on our prototype implementation

shows that PerfGreen with its energy-aware scheduler and resource allocator on

average reduces energy usage by 53%, improves performance per watt by 64%

and server density by 25% while keeping performance deviations to a minimum.

Place, publisher, year, edition, pages
2018.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-145925OAI: oai:DiVA.org:umu-145925DiVA, id: diva2:1192262
Conference
to be submitted
Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-06-09
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

Open Access in DiVA

No full text in DiVA

Authority records BETA

Tesfatsion, Selome KostentinosWadbro, EddieTordsson, Johan

Search in DiVA

By author/editor
Tesfatsion, Selome KostentinosWadbro, EddieTordsson, Johan
By organisation
Department of Computing Science
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 452 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf