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k-HEFT: A static task scheduling algorithm in clouds

Published: 19 May 2018 Publication History

Abstract

In the research of task scheduling in the cloud environment, in order to minimize the task scheduling length, an efficient scheduling strategy is critical. A scheduling algorithm based on Top-k strategy is proposed to solve the problem of workflow scheduling with minimizing the completion time in cloud computing. In each assignment, choose the most k schedulable tasks in priority sequence in turn to try to allocate, and selecte the task that with the earliest completion time. The experimental results show that with HEFT and IHEFT algorithm, k-HEFT algorithm can reduce the scheduling length effectively.

References

[1]
Sabelfeld, A., and A. C. Myers. 2003. Language-based information-flow security. IEEE Journal on Selected Areas in Communications. 21, 1(2003), 5--19.
[2]
Smith, Geoffrey. 2008. Improved typings for probabilistic noninterference in a multi-threaded language. Journal of Computer Security.14,6(2008), 591--623.
[3]
Tian G Z, Xiao C B, Zhao J J.2014. Evolutionary algorithm towards resource allocation of concurrent scheduling multiple DAGs in clouds. Application Research of Computers. 31, 9 (2014), 2798--2802.
[4]
Cao H, Jin H, Wu X, et al. 2010. DAGMap: efficient and dependable scheduling of DAG workflow job in Grid. Journal of Supercomputing. 51,2(2010), 201--223.
[5]
Baskiyar, S. and Palli, K. K. 2006. Low power scheduling of dags to minimize finish times. In International Conference on High-Performance Computing. (December, 2006).Springer, Berlin, Heidelberg, 353--362.
[6]
Daoud M I, Kharma N. 2008. A high performance algorithm for static task scheduling in heterogeneous distributed computing systems. Journal of Parallel & Distributed Computing. 68,4(2008), 399--409.
[7]
Salman A, Ahmad I, Al-Madani S. 2002. Particle swarm optimization for task assignment problem. Microprocessors & Microsystems. 26,8(2002), 363--371.
[8]
Topcuoglu, H., S. Hariri, and M. Y. Wu. 2002. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel & Distributed Systems. 13, 3(2002), 260--274.
[9]
Wang X L, Huang H B, Deng S. 2012. List Scheduling Algorithm for Static Task with Precedence Constraints for Cyber-physical Systems. Acta Automatica Sinica. 38, 11 (2012), 1870--1879.
[10]
Zhao, H. and Sakellariou, R. 2003. An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm. In European Conference on Parallel Processing (Klagenfurt, Austria, August 26-29, 2003). Springer, Berlin, Heidelberg, 189--194.
[11]
Decker, J.and Schneider, J. 2007. Heuristic scheduling of grid workflows supporting co-allocation and advance reservation. In Seventh IEEE International Symposium on Cluster Computing and the Grid, (May, 2007). IEEE, Washington. DC, USA, 335--342.
[12]
Wieczorek M, Siddiqui M, Villazon A, et al. 2006. Applying advance reservation to increase predictability of workflow execution on the grid. In Second IEEE International Conference on e-Science and Grid Computing, (December, 2006). e-Science'06, IEEE, Washington. DC, USA, 82--82.
[13]
Lin, C. 2010. Scientific workflow integration for services computing. Doctoral dissertation. UMI Number: 3418291., Wayne State University.
[14]
Yan G, Jiong Y U, Yang X. 2013. Two-step task scheduling strategy for scientific workflow on cloud computing platform. Journal of Computer Applications. 33.4(2013), 1006--1000.
[15]
Zhu J Y, Xiao D. 2013. Path priority-based heuristic task scheduling algorithm for cloud computing. Computer Engineering & Design. 34, 10(2013), 3511--3515.
[16]
Liu K K. 2010. High Performance Algorithm for Task Scheduling in Heterogeneous Environment. Computer Systems & Applications. 19, 11 (2010), 102--107.
[17]
Liu Y, Shao H, Jing W. 2014. DAG Task Scheduling Integrating with Security and Availability in Cloud Environment. Computer Engineering. 40,12,(2014),12- 18.
[18]
Li K, Zhang G, Zhu Z. 2016. A decomposition-based multi-objective workflow scheduling algorithm in cloud environment. Computer Engineering & Science. 38,8(2016), 1588--1594.
[19]
Ma J, Yin J. 2014. Security-constrained workflow scheduling in cloud computing environments. Computer Engineering & Science. 36,4 (2014), 607--617.
[20]
Sun Y, Yu J, Zhu J B.2014. Preemptive Scheduling for Multiple DAGs in Cloud Computing. Computer Science. 41, 3 (2014), 145--148.
[21]
Liu K, Jin H, Chen J, et al. 2010. A Compromised- Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform. International Journal of High Performance Computing Applications. 24,4(2010), 445--456.
[22]
Shi Z, Dongarra J J. 2006.Scheduling workflow applications on processors with different capabilities. Future Generation Computer Systems. 22, 6(2006), 665--675.
[23]
Gong, Y. and Pierce, M. E. and Fox, G. C. 2009. Dynamic resource-critical workflow scheduling in heterogeneous environments. In Workshop on Job Scheduling Strategies for Parallel Processing.(Rome, Italy, May 29,2009). Springer, Berlin, Heidelberg, 1--15.
[24]
Guo H, Chen Z, Yu Y, et al. A communication aware DAG workflow cost optimization model and algorithm{J}. Journal of Computer Research & Development, 2015, 52(6):1400--1408.
[25]
Jiang Y, Sun G, Yinlong X U. An Effective Task Scheduling Algorithm for Heterogeneous Parallel Systems{J}. Computer Engineering, 2007, 33(11):39--41.

Cited By

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  • (2023)MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithmComputing10.1007/s00607-023-01175-9105:10(2119-2142)Online publication date: 18-Apr-2023
  • (2021)A novel cloud workflow scheduling algorithm based on stable matching game theoryThe Journal of Supercomputing10.1007/s11227-021-03742-3Online publication date: 27-Mar-2021
  • (2019)Scheduling Method Based on Backfill Strategy for Multiple DAGs in Cloud ComputingData Science10.1007/978-981-15-0121-0_21(278-290)Online publication date: 13-Sep-2019

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cover image ACM Other conferences
ICIIP '18: Proceedings of the 3rd International Conference on Intelligent Information Processing
May 2018
249 pages
ISBN:9781450364966
DOI:10.1145/3232116
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2018

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Author Tags

  1. Cloud computing
  2. DAG
  3. HEFT
  4. Makespan
  5. Static tasks scheduling

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ICIIP '18

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Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

View all
  • (2023)MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithmComputing10.1007/s00607-023-01175-9105:10(2119-2142)Online publication date: 18-Apr-2023
  • (2021)A novel cloud workflow scheduling algorithm based on stable matching game theoryThe Journal of Supercomputing10.1007/s11227-021-03742-3Online publication date: 27-Mar-2021
  • (2019)Scheduling Method Based on Backfill Strategy for Multiple DAGs in Cloud ComputingData Science10.1007/978-981-15-0121-0_21(278-290)Online publication date: 13-Sep-2019

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