SLA-Aware Resource Scaling for Energy Efficiency

Eidah J. Alzahrani; Zahir Tari; Panlop Zeephongsekul; Young Choon Lee; Deafallah Alsadie; Albert Y. Zomaya

doi:10.1109/HPCC-SmartCity-DSS.2016.0123

SLA-Aware Resource Scaling for Energy Efficiency

Eidah J. Alzahrani, Zahir Tari, Panlop Zeephongsekul, Young Choon Lee, Deafallah Alsadie, Albert Y. Zomaya

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

Abstract

Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach - that takes into account Service Level Agreement (SLA) - for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.

Original language	English
Title of host publication	Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016
Editors	Jinjun Chen, Laurence T. Yang
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	852-859
Number of pages	8
ISBN (Electronic)	9781509042968
DOIs	https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0123
Publication status	Published - Dec 2016
Event	18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 - Sydney, Australia Duration: 12 Dec 2016 → 14 Dec 2016

Other

Other	18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016
Country/Territory	Australia
City	Sydney
Period	12/12/16 → 14/12/16

Keywords

Auto-Scaling
Cloud Data Centers
Docker Containers
Energy Efficiency
Resource Provisioning

Access to Document

10.1109/HPCC-SmartCity-DSS.2016.0123

Cite this

Alzahrani, E. J., Tari, Z., Zeephongsekul, P., Lee, Y. C., Alsadie, D., & Zomaya, A. Y. (2016). SLA-Aware Resource Scaling for Energy Efficiency. In J. Chen, & L. T. Yang (Eds.), Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 (pp. 852-859). Article 7828464 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0123

Alzahrani, Eidah J. ; Tari, Zahir ; Zeephongsekul, Panlop et al. / SLA-Aware Resource Scaling for Energy Efficiency. Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016. editor / Jinjun Chen ; Laurence T. Yang. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 852-859

@inproceedings{b27317baf8764fa09aaa83513168625e,

title = "SLA-Aware Resource Scaling for Energy Efficiency",

abstract = "Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach - that takes into account Service Level Agreement (SLA) - for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.",

keywords = "Auto-Scaling, Cloud Data Centers, Docker Containers, Energy Efficiency, Resource Provisioning",

author = "Alzahrani, {Eidah J.} and Zahir Tari and Panlop Zeephongsekul and Lee, {Young Choon} and Deafallah Alsadie and Zomaya, {Albert Y.}",

year = "2016",

month = dec,

doi = "10.1109/HPCC-SmartCity-DSS.2016.0123",

language = "English",

pages = "852--859",

editor = "Jinjun Chen and Yang, {Laurence T.}",

booktitle = "Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 ; Conference date: 12-12-2016 Through 14-12-2016",

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Alzahrani, EJ, Tari, Z, Zeephongsekul, P, Lee, YC, Alsadie, D & Zomaya, AY 2016, SLA-Aware Resource Scaling for Energy Efficiency. in J Chen & LT Yang (eds), Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016., 7828464, Institute of Electrical and Electronics Engineers (IEEE), pp. 852-859, 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, Sydney, Australia, 12/12/16. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0123

SLA-Aware Resource Scaling for Energy Efficiency. / Alzahrani, Eidah J.; Tari, Zahir; Zeephongsekul, Panlop et al.
Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016. ed. / Jinjun Chen; Laurence T. Yang. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 852-859 7828464.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - SLA-Aware Resource Scaling for Energy Efficiency

AU - Alzahrani, Eidah J.

AU - Tari, Zahir

AU - Zeephongsekul, Panlop

AU - Lee, Young Choon

AU - Alsadie, Deafallah

AU - Zomaya, Albert Y.

PY - 2016/12

Y1 - 2016/12

N2 - Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach - that takes into account Service Level Agreement (SLA) - for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.

AB - Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach - that takes into account Service Level Agreement (SLA) - for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.

KW - Auto-Scaling

KW - Cloud Data Centers

KW - Docker Containers

KW - Energy Efficiency

KW - Resource Provisioning

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DO - 10.1109/HPCC-SmartCity-DSS.2016.0123

M3 - Conference proceeding contribution

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BT - Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016

A2 - Chen, Jinjun

A2 - Yang, Laurence T.

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016

Y2 - 12 December 2016 through 14 December 2016

ER -

Alzahrani EJ, Tari Z, Zeephongsekul P, Lee YC, Alsadie D, Zomaya AY. SLA-Aware Resource Scaling for Energy Efficiency. In Chen J, Yang LT, editors, Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 852-859. 7828464 doi: 10.1109/HPCC-SmartCity-DSS.2016.0123

SLA-Aware Resource Scaling for Energy Efficiency

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