Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (8): 112-118.doi: 10.11896/jsjkx.200300038

;

Previous Articles     Next Articles

Energy Efficient Scheduling Algorithm of Workflows with Cost Constraint in Heterogeneous Cloud Computing Systems

ZHANG Long-xin, ZHOU Li-qian, WEN Hong, XIAO Man-sheng, DENG Xiao-jun   

  1. School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:ZHANG Long-xin, born in 1983, Ph.D, associate professor, master supervisor, is a member of China Computer Federation.His main research interests include cloud computing, scheduling for distri-buted computing systems and big data analysis.
    WEN Hong, born in 1981, Ph.D, asso-ciate professor, master supervisor, is a member of China Computer Federation.His main research interests include deep learning and network virtualization.
  • Supported by:
    This work was supported by the National Key R&D Program of China(2018YFB1003401), National Natural Science Foundation of China(61702178, 61672224), Natural Science Foundation of Hunan Province(2019JJ50123, 2020JJ6087, 2019JJ60054, 2018JJ4068) and China Scholarship Council(201808430297).

PDF (PC)

718

Abstract: Cloud computing has become a very important computing service mode in various industries.Traditional studies on cloud computing mainly focus on the research of service quality such as the pricing mode, profit maximization and execution efficiency of cloud services.Green computing is the development trend of distributed computing.Aiming at the scheduling problem of workflow task set that meets the computing cost constraint of cloud users in heterogeneous cloud environment, an energy-aware based on budget level scheduling algorithm(EABL) with low time complexity is proposed.The EABL algorithm consists of three main stages:task priority establishment, task budget cost allocation, optimal execution virtual machine and energy efficiency frequency selection of the parallel task set, so as to minimize the energy consumption during task set execution under the constraint of budget cost.A large-scale workflow task sets in the real world are used to conduct a large number of tests on the algorithm for the experiment in this paper.Compared with famous algorithms EA_HBCS and MECABP, EABL algorithm can effectively reduce the energy consumption in the computing process of cloud data centers by making full use of the budget cost.

Key words: Budget constraint, Energy efficiency, Heterogeneous computing, Task scheduling, Workflow scheduling

CLC Number: 

  • TP301
[1] SHARMA N K, REDDY G R M.Multi-objective energy efficient virtual machines allocation at the cloud data center[J].IEEE Transactions on Services Computing, 2019, 12(1):158-171.
[2] ZHANG L X, LI K L, XU Y M, et al.Maximizing reliability with energy conservation for parallel task scheduling in a hete-rogeneous cluster[J].Information Sciences, 2015, 319:113-131.
[3] MANSOURI Y, TOOSI A N, BUYYA R.Cost optimization fordynamic replication and migration of data in cloud data centers[J].IEEE Transactions on Cloud Computing, 2019, 7(3):705-718.
[4] LI K Q.Scheduling parallel tasks with energy and time con-straints on multiple manycore processors in a cloud computing environment[J].Future Generation Computer Systems, 2018, 82:591-605.
[5] ZHANG X M, JIA M, GU X M, et al.An energy efficient resource allocation scheme based on cloud-computing in H-CRAN[J].IEEE Internet of Things Journal, 2019, 6(3):4968-4976.
[6] ZHANG L X, LI K L, ZHENG W H, et al.Contention-aware reliability efficient scheduling on heterogeneous computing systems[J].IEEE Transactions on Sustainable Computing, 2018, 3(3):182-194.
[7] JUAREZ F, EJARQUE J, BADIA R M.Dynamic energy-aware scheduling for parallel task-based application in cloud computing[J].Future Generation Computer Systems, 2018, 78:257-271.
[8] ZHANG L X, LI K L, LI C Y, et al.Bi-objective workflowscheduling of the energy consumption and reliability in heterogeneous computing systems[J].Information Sciences, 2017, 379:241-256.
[9] SHOJAFAR M, CORDESCHI N, BACCARELLI E.Energy-efficient adaptive resource management for real-time vehicular cloud services[J].IEEE Transactions on Cloud Computing, 2019, 7(1):196-209.
[10]PENG H, WEN W S, TSENG M L, et al.Joint optimizationmethod for task scheduling time and energy consumption in mobile cloud computing environment[J].Applied Soft Computing, 2019, 80:534-545.
[11]LI Z, GE J, HU H, et al.Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds[J].IEEE Transactions on Services Computing, 2018, 11(4):713-726.
[12]ABRISHAMI S, NAGHIBZADEH M, EPEMA D H J.Dead-line-constrained workflow scheduling algorithms for infrastructure as a service clouds[J].Future Generation Computer Systems, 2013, 29(1):158-169.
[13]ARABNEJAD H, BARBOSA J G.A budget constrained schedu-ling algorithm for workflow applications[J].Journal of Grid Computing, 2014, 12(4):665-679.
[14]CHEN Y K, XIE G Q, LI R F.Reducing energy consumption with cost budget using available budget preassignment inhete-rogeneous cloud computing systems[J].IEEE Access, 2018, 6:20572-20583.
[1] LIN Chao-wei, LIN Bing, CHEN Xing. Study on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment [J]. Computer Science, 2022, 49(2): 312-320.
[2] TAN Shuang-jie, LIN Bao-jun, LIU Ying-chun, ZHAO Shuai. Load Scheduling Algorithm for Distributed On-board RTs System Based on Machine Learning [J]. Computer Science, 2022, 49(2): 336-341.
[3] SHEN Biao, SHEN Li-wei, LI Yi. Dynamic Task Scheduling Method for Space Crowdsourcing [J]. Computer Science, 2022, 49(2): 231-240.
[4] CHEN Yong, XU Qi, WANG Xiao-ming, GAO Jin-yu, SHEN Rui-juan. Energy Efficient Power Allocation for MIMO-NOMA Communication Systems [J]. Computer Science, 2021, 48(6A): 398-403.
[5] WANG Zheng, JIANG Chun-mao. Cloud Task Scheduling Algorithm Based on Three-way Decisions [J]. Computer Science, 2021, 48(6A): 420-426.
[6] CHENG Yun-fei, TIAN Hong-xin, LIU Zu-jun. Collaborative Optimization of Joint User Association and Power Control in NOMA Heterogeneous Network [J]. Computer Science, 2021, 48(3): 269-274.
[7] XIE Jing-ming, HU Wei-fang, HAN Lin, ZHAO Rong-cai, JING Li-na. Quantum Fourier Transform Simulation Based on “Songshan” Supercomputer System [J]. Computer Science, 2021, 48(12): 36-42.
[8] CAI Ling-feng, WEI Xiang-lin, XING Chang-you, ZOU Xia, ZHANG Guo-min. Failure-resilient DAG Task Rescheduling in Edge Computing [J]. Computer Science, 2021, 48(10): 334-342.
[9] MA Yu-yin, ZHENG Wan-bo, MA Yong, LIU Hang, XIA Yun-ni, GUO Kun-yin, CHEN Peng, LIU Cheng-wu. Multi-workflow Offloading Method Based on Deep Reinforcement Learning and ProbabilisticPerformance-awarein Edge Computing Environment [J]. Computer Science, 2021, 48(1): 40-48.
[10] YANG Wang-dong, WANG Hao-tian, ZHANG Yu-feng, LIN Sheng-le, CAI Qin-yun. Survey of Heterogeneous Hybrid Parallel Computing [J]. Computer Science, 2020, 47(8): 5-16.
[11] SUN Min, CHEN Zhong-xiong, YE Qiao-nan. Workflow Scheduling Strategy Based on HEDSM Under Cloud Environment [J]. Computer Science, 2020, 47(6): 252-259.
[12] HU Jun-qin, ZHANG Jia-jun, HUANG Yin-hao, CHEN Xing, LIN Bing. Computation Offloading Scheduling Technology for DNN Applications in Edge Environment [J]. Computer Science, 2020, 47(10): 247-255.
[13] ZHANG Zhou, HUANG Guo-rui, JIN Pei-quan. Task Scheduling on Storm:Current Situations and Research Prospects [J]. Computer Science, 2019, 46(9): 28-35.
[14] ZHAO Lei, ZHOU Jin-he. ICN Energy Efficiency Optimization Strategy Based on Content Field of Complex Networks [J]. Computer Science, 2019, 46(9): 137-142.
[15] ZENG Jin-jing, ZHANG Jian-shan, LIN Bing, ZHANG Wen-de. Cloudlet Workload Balancing Algorithm in Wireless Metropolitan Area Networks [J]. Computer Science, 2019, 46(8): 163-170.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.