面向数据密集型工作流的能耗感知调度策略

doi:10.11959/j.issn.1000-436x.2015017

Abstract

Abstract:

With the increasing scale of data centers,high energy consumption has become a critical issue in high-performance computing area.To address the issue of energy consumption optimization for data-intensive workflow applications,a set of virtual data-accessing nodes are introduced into the original workflow for quantitatively evaluating the data-accessing energy consumption,by which a novel heuristic policy called minimal energy consumption path is designed.Based on the proposed heuristic policy,two energy-aware scheduling algorithms are implemented,which are deprived from the classical HEFT and CPOP scheduling algorithms.Extensive experiments are conducted to investigate the performance of the proposed algorithms,and the results show that they can significantly reduce the data-accessing energy consumption.Also,the proposed algorithms show better adaptive when the system is in presence of large-scale workflows.

Key words: workflow, energy consumption, heuristic policy, cloud computing

Peng XIAO,Zhi-gang HU,Xi-long QU. Energy-aware scheduling policy for data-intensive workflow[J]. Journal on Communications, 2015, 36(1): 149-158.

Figures/Tables 6

References 21

[1]	胡定磊，陈书明．低功耗编译技术综述[J]．电子学报， 2005,33(4):676-682. HU D L , CHEN S M . Low power/energy compilation technology[J]. Acta Electronica Sinica, 2005,33(4):676-682.
[2]	KANT K , MURUGAN M , DU D C . Willow:a control system for energy and thermal adaptive computing[A]. Proceedings of IEEE International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2011. 36-47.
[3]	林闯，田源，姚敏．绿色网络和绿色评价：节能机制、模型和评价[J]．计算机学报， 2011,34(4):593-612. LIN C , TIAN Y , YAO M . Green network and green evaluation:mechanism,modeling and evaluation[J]. Chinese Journal of Computers, 2011,34(4):593-612.
[4]	HIKITA J , HIRANO A , NAKASHIMA H . Saving 200kW and $200 K/year by power-aware job/machine scheduling[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2008. 1-8.
[5]	KONDO M , IKEDA Y , NAKAMURA H . High performance cluster system design by adaptive power control[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2007. 1-8.
[6]	WANG L , LASZEWSKI G , DAYAL J , et al. Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with DVFS[A]. Proceedings of IEEE/ACM International Conference on Cluster,Cloud and Grid Computing[C]. Washington,USA, 2010. 368-377.
[7]	RIZVANDI N B , TAHERI J , ZOMAYA A Y , et al. Linear combinations of DVFS-enabled processor frequencies to modify the energy-aware scheduling algorithms[A]. Proceedings of IEEE/ACM International Conference on Cluster,Cloud and Grid Computing[C]. Washington,USA, 2010. 388-397.
[8]	GARG R , SON S W , KANDEMIR M , et al. Markov model based disk power management for data intensive workloads[A]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid[C]. Washington,USA, 2009. 76-83.
[9]	HU F P , EVANS J J . Power and environment aware control of beowulf clusters[J]. Cluster Computing, 2009,12(3): 299-308.
[10]	SONG S , SU C Y , GE R , et al. Iso-energy-efficiency:an approach to power-constrained parallel computation[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2011. 128-139.
[11]	CHO S , MELHEM R G . On the interplay of parallelization,program performance,and energy consumption[J]. IEEE Transactions on Parallel and Distributed Systems, 2010,21(3): 342-353.
[12]	BENOIT A , GOUD P R , ROBERT Y . Performance and energy optimization of concurrent pipelined applications[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2010. 1-12.
[13]	SHANG P , WANG J . A novel power management for cmp systems in data-intensive environment[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2011. 92-103.
[14]	HERATH C , PLALE B . Streamflow-programming model for data streaming in scientific workflows[A]. Proceedings of IEEE/ACM International Conference on Cluster,Cloud and Grid Computing[C]. Washington,USA, 2010. 302-311.
[15]	KHARGHARIA B , HARIRI S , SZIDAROVSZKY F , et al. Autonomic power and performance management for large-scale data centers[A]. Proceedings of International Parallel and Distributed Processing Symposium[C]. Washington,USA, 2007. 1-8.
[16]	王桂彬，杨学军，徐新海等．异构系统功耗感知的并行循环调度方法[J]．软件学报， 2011,22(9):2222-2234. WANG G B , YANG X J , XU X B , et al. Power-aware parallel loop scheduling method for heterogeneous system[J]. Journal of Software, 2011,22(9):2222-2234.
[17]	NIYATO D , CHAISIRI S , SUNG L B . Optimal power management for server farm to support green computing[A]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid[C]. Washington,USA, 2009. 84-91.
[18]	LEE Y C , ZOMAYA A Y . Energy conscious scheduling for distributed computing systems under different operating conditions[J]. IEEE Transactions on Parallel and Distributed Systems, 2011,22(8): 1374-1381.
[19]	TOPCUOGLU H , HARIRI S , WU M Y . Performance-effective and low-complexity task scheduling for heterogeneous computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2002,13(2): 260-274.
[20]	CALHEIROS R N , RANJAN R , BELOGLAZOV A , et al. BELOGLAZOV A,et al.CloudSim:a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software:Practice and Experience, 2011,41(1): 23-50.
[21]	BLAHA P , SCHWARZ K , MADSEN G , et al. WIEN2K:An Augmented Plane Wave Plus Local Orbitals Program for Calculating Crystal Properties[M]. Vienna:Institute of Physical and Theoretical Chemistry, 2001.

Metrics

Recommended 0

No Suggested Reading articles found!

Energy-aware scheduling policy for data-intensive workflow

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 21

Related Articles 15

Metrics

Recommended 0

[1]	Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG. Scheduling framework based on reinforcement learning in online-offline colocated cloud environment [J]. Journal on Communications, 2023, 44(6): 90-102.
[2]	Weifeng LU, Ning LI, Jia XU, Lijie XU, Jian XU. Joint scheduling algorithm for correlative tasks in multi-access edge computing [J]. Journal on Communications, 2023, 44(4): 87-98.
[3]	Xueyong YU, Lixiang QIU, Jianing SONG, Hongbo ZHU. Security communication and energy efficiency optimization strategy in UAV-aided edge computing [J]. Journal on Communications, 2023, 44(3): 45-54.
[4]	Cuiran LI, Xuejie WANG, Jianli XIE, Anqi LYU. Routing algorithm for railway monitoring linear WSN based on improved PSO [J]. Journal on Communications, 2022, 43(5): 155-165.
[5]	Huaqun WANG, Zhe LIU, Debiao HE, Jiguo LI. Identity-based provable data possession scheme for multi-source IoT terminal data in public cloud [J]. Journal on Communications, 2021, 42(7): 52-60.
[6]	Jianhong ZHANG, Menglong WU, Jing WANG, Pei LIU, Zhengtao JIANG, Changgen PENG. Secure and verifiable multi-keyword searchable encryption scheme in cloud [J]. Journal on Communications, 2021, 42(4): 139-149.
[7]	Ruiqi LI, Chunfu JIA, Yafei WANG. Multi-key homomorphic proxy re-encryption scheme based on NTRU and its application [J]. Journal on Communications, 2021, 42(3): 11-22.
[8]	Jiawei ZHANG, Jianfeng MA, Zhuo MA, Teng LI. Time-based and privacy protection revocable and traceable data sharing scheme in cloud computing [J]. Journal on Communications, 2021, 42(10): 81-94.
[9]	Wenjuan WANG, Xuehui DU, Dibin SHAN. Construction method of attack scenario in cloud environment based on dynamic probabilistic attack graph [J]. Journal on Communications, 2021, 42(1): 1-17.
[10]	Youliang TIAN,Qin LUO. Verifiable multi-keyword search scheme based on improved Merkle-Tree authentication method [J]. Journal on Communications, 2020, 41(9): 118-129.
[11]	Na WANG,Kun ZHENG,Junsong FU,Jian LI. Method of ciphertext retrieval in mobile edge computing based on block segmentation [J]. Journal on Communications, 2020, 41(7): 95-102.
[12]	Lindong ZHAO,Wenqin ZHUANG,Jianxin CHEN,Liang ZHOU. Hierarchical task offloading in heterogeneous cellular network:modeling and optimization [J]. Journal on Communications, 2020, 41(4): 34-44.
[13]	Bing LIANG,Wen JI. Multiuser computation offloading for edge-cloud collaboration using submodular optimization [J]. Journal on Communications, 2020, 41(10): 25-36.
[14]	SU Mingfeng,WANG Guojun,LI Renfa. Multidimensional QoS cloud computing resource scheduling method based on stakeholder perspective [J]. Journal on Communications, 2019, 40(6): 102-115.
[15]	CHEN Xingshu,HUA Qiang,WANG Yitong,GE Long,ZHU Yi. Research on low-rate DDoS attack of SDN network in cloud environment [J]. Journal on Communications, 2019, 40(6): 210-222.