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HOPE: Enabling Efficient Service Orchestration in Software-Defined Data Centers

Authors:

Tao LiAuthors Info & Claims

ICS '16: Proceedings of the 2016 International Conference on Supercomputing

Article No.: 10, Pages 1 - 12

https://doi.org/10.1145/2925426.2926257

Published: 01 June 2016 Publication History

Abstract

The functional scope of today's software-defined data centers (SDDC) has expanded to such an extent that servers face a growing amount of critical background operational tasks like load monitoring, logging, migration, and duplication, etc. These ancillary operations, which we refer to as management operations, often nibble the stringent data center power envelope and exert a tremendous amount of pressure on front-end user tasks. However, existing power capping, peak shaving, and time shifting mechanisms mainly focus on managing data center power demand at the "macro level" -- they do not distinguish ancillary background services from user tasks, and therefore often incur significant performance degradation and energy overhead.

In this study we explore "micro-level" power management in SDDC: tuning a specific set of critical loads for the sake of overall system efficiency and performance. Specifically, we look at management operations that can often lead to resource contention and energy overhead in an IaaS SDDC. We assess the feasibility of this new power management paradigm by characterizing the resource and power impact of various management operations. We propose HOPE, a new system optimization framework for eliminating the potential efficiency bottleneck caused by the management operations in the SDDC. HOPE is implemented on a customized OpenStack cloud environment with heavily instrumented power infrastructure. We thoroughly validate HOPE models and optimization efficacy under various user workload scenarios. Our deployment experiences show that the proposed technique allows SDDC to reduce energy consumption by 19%, reduce management operation execution time by 25.4%, and in the meantime improve workload performance by 30%.

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Cited By

Ahmad SArif MAhmad JNazim MMehfuz S(2024)Convergent encryption enabled secure data deduplication algorithm for cloud environmentConcurrency and Computation: Practice and Experience10.1002/cpe.820536:21Online publication date: 21-Jun-2024
https://doi.org/10.1002/cpe.8205
Patra SJena SMohanty JGourisaria M(2021)DedupCloud: an optimized efficient virtual machine deduplication algorithm in cloud computing environmentData Deduplication Approaches10.1016/B978-0-12-823395-5.00009-4(281-306)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-823395-5.00009-4
Montazerolghaem A(2020)Software-defined load-balanced data center: design, implementation and performance analysisCluster Computing10.1007/s10586-020-03134-xOnline publication date: 13-Jul-2020
https://doi.org/10.1007/s10586-020-03134-x
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cover image ACM Conferences

ICS '16: Proceedings of the 2016 International Conference on Supercomputing

June 2016

547 pages

ISBN:9781450343619

DOI:10.1145/2925426

Copyright © 2016 ACM.

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Published: 01 June 2016

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ICS '16: 2016 International Conference on Supercomputing

June 1 - 3, 2016

Istanbul, Turkey

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Cited By

Ahmad SArif MAhmad JNazim MMehfuz S(2024)Convergent encryption enabled secure data deduplication algorithm for cloud environmentConcurrency and Computation: Practice and Experience10.1002/cpe.820536:21Online publication date: 21-Jun-2024
https://doi.org/10.1002/cpe.8205
Patra SJena SMohanty JGourisaria M(2021)DedupCloud: an optimized efficient virtual machine deduplication algorithm in cloud computing environmentData Deduplication Approaches10.1016/B978-0-12-823395-5.00009-4(281-306)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-823395-5.00009-4
Montazerolghaem A(2020)Software-defined load-balanced data center: design, implementation and performance analysisCluster Computing10.1007/s10586-020-03134-xOnline publication date: 13-Jul-2020
https://doi.org/10.1007/s10586-020-03134-x
Zhang LLi CWang PLiu YHu YChen QGuo M(2019)Characterizing and orchestrating NFV-ready servers for efficient edge data processingProceedings of the International Symposium on Quality of Service10.1145/3326285.3329057(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3326285.3329057
Kaur RChana IBhattacharya J(2018)Data deduplication techniques for efficient cloud storage managementThe Journal of Supercomputing10.1007/s11227-017-2210-874:5(2035-2085)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11227-017-2210-8
Hu YSong MLi T(2017)Towards "Full Containerization" in Containerized Network Function VirtualizationACM SIGARCH Computer Architecture News10.1145/3093337.303771345:1(467-481)Online publication date: 4-Apr-2017
https://dl.acm.org/doi/10.1145/3093337.3037713
Hu YSong MLi T(2017)Towards "Full Containerization" in Containerized Network Function VirtualizationACM SIGPLAN Notices10.1145/3093336.303771352:4(467-481)Online publication date: 4-Apr-2017
https://dl.acm.org/doi/10.1145/3093336.3037713
Hu YSong MLi T(2017)Towards "Full Containerization" in Containerized Network Function VirtualizationACM SIGOPS Operating Systems Review10.1145/3093315.303771351:2(467-481)Online publication date: 4-Apr-2017
https://doi.org/10.1145/3093315.3037713
Hu YSong MLi TChen YTemam OCarter J(2017)Towards "Full Containerization" in Containerized Network Function VirtualizationProceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3037697.3037713(467-481)Online publication date: 4-Apr-2017
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