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Multiobjective differential evolution for workflow execution on grids

Published: 26 November 2007 Publication History

Abstract

Most algorithms developed for scheduling applications on global Grids focus on a single Quality of Service (QoS) parameter such as execution time, cost or total data transmission time. However, if we consider more than one QoS parameter (eg. execution cost and time may be in conflict) then the problem becomes more challenging. To handle such scenarios, it is convenient to use heuristics rather than a deterministic algorithm. In this paper we have proposed a workflow execution planning approach using Multiobjective Differential Evolution (MODE). Our goal was to generate a set of trade-off schedules according to two user specified QoS requirements (time and cost). The alternative tradeoff solutions offer more flexibility to users when estimating their QoS requirements of workflow executions. We have compared our results with two baseline multiobjective evolutionary algorithms. Simulation results show that our modified MODE is able to find a comparatively better spread of compromise solutions.

References

[1]
H. A. Abbass, R. Sarkar, and C. Newton. A Pareto Differential Evolution Approach to Vector Optimisation Problems. In IEEE Congress on Evolutionary Computation. IEEE Press, 2001.
[2]
D. N. Bhat. An Evolutionary Measure for Image Matching. In 14th International Conference on Pattern Recognition, pages 850--852. IEEE Press, August 1998.
[3]
E. Deelman. Mapping Abstract Complex Workflows Onto Grid Environments. Journal of Grid Computing, 1:25--39.
[4]
I. Foster and C. Kesselman. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco, California.
[5]
X. He, X. H. Sun, and G. V. Laszewski. QoS Guided Min-Min Heuristic for Grid Task Scheduling. Journal of Computer Science and Technology, 18(4):442--451.
[6]
S. Huband, P. Hingston, L. Barone, and L. While. A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Transaction on Evolutionary Computation, 10(5):477--506.
[7]
J. Knowles and D. Corne. The Pareto Archived Evolution Strategy : A New Baseline Algorithm for Pareto Multiobjective Optimisation. In The Congress on Evolutionary Computation, pages 98--105. IEEE Press, 1999.
[8]
A. C. Nearchou and S. L. Omirou. Differential Evolution for Sequencing and Scheduling Optimization. Journal of Heuristics, 12:395--411.
[9]
G. Onwubolu and D. Davendra. Scheduling Flowshops using Differential Evolution Algorithm. Europian Journal of Operational Research, 171:674--692.
[10]
R. Prodan and T. Fahringer. Dynamic Scheduling of Scientific Workflow Applications on The Grid: A Case Study. In ACM symposium on Applied computing. ACM Press, 2005.
[11]
R. Storn and K. Price. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. Journal of Global Optimization, 11:241--354.
[12]
T. Tsuchiya, T. Osada, and T. Kikuno. Genetic-Based Multiprocessor Scheduling using Task Duplication. Microprocessors and Microsystems, 22:197--207.
[13]
L. Wang, H. J. Siegel, V. P. Roychowdhury, and A. A. Maciejewski. Task Matching and Scheduling in Heterogeneous Computing Environments using a Genetic-Algorithm-Based Approach. Journal of Parallel Distributed Computing, 47:9--22.
[14]
A. S. Wu, H. Yu, S. Jin, K.-C. Lin, and G. Schiavone. An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling. IEEE Transaction on Parallel and Distributed Systems, 15(9):824--834.
[15]
M. Wu and D. Gajski. Hypertool: A Programming Aid for Message-Passing Systems. IEEE Transaction on Parallel and Distributed Systems, 1(3):330--343.
[16]
G. Ye, R. Rao, and M. Li. A Multiobjective Resource Scheduling Approach Based on Genetic Algorithms in Grid Environment. In 5th International Conference on Grid and Cooperative Workshops, 2006.
[17]
J. Yu and R. Buyya. Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms. Scientific Programming, 14:217--230.
[18]
J. Yu, M. Kirley, and R. Buyya. Multi-objective Planning for Workflow Execution on Grids. In 8th IEEE/ACM International Conference on Grid Computing, September 2007.

Cited By

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  • (2013)Workflow Scheduling with Fault ToleranceNetwork and Traffic Engineering in Emerging Distributed Computing Applications10.4018/978-1-4666-1888-6.ch005(94-123)Online publication date: 2013
  • (2013)Meta-schedulers for grid computing based on multi-objective swarm algorithmsApplied Soft Computing10.1016/j.asoc.2012.12.03013:4(1567-1582)Online publication date: 1-Apr-2013
  • (2012)A Scheduler for Grid Task Based on Differential Evolution Algorithm and Robust to Uncertainty Communication Demand of the ApplicationProceedings of the 2012 12th International Conference on Computational Science and Its Applications10.1109/ICCSA.2012.11(1-6)Online publication date: 18-Jun-2012
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Published In

cover image ACM Other conferences
MGC '07: Proceedings of the 5th international workshop on Middleware for grid computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
November 2007
64 pages
ISBN:9781595939449
DOI:10.1145/1376849
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2007

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Author Tags

  1. grid scheduling
  2. multiobjective differential evolution
  3. multiobjective optimization

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  • Research-article

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Middleware07
Middleware07: 8th International Middleware Conference
November 26 - 30, 2007
California, Newport Beach

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Overall Acceptance Rate 14 of 36 submissions, 39%

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Cited By

View all
  • (2013)Workflow Scheduling with Fault ToleranceNetwork and Traffic Engineering in Emerging Distributed Computing Applications10.4018/978-1-4666-1888-6.ch005(94-123)Online publication date: 2013
  • (2013)Meta-schedulers for grid computing based on multi-objective swarm algorithmsApplied Soft Computing10.1016/j.asoc.2012.12.03013:4(1567-1582)Online publication date: 1-Apr-2013
  • (2012)A Scheduler for Grid Task Based on Differential Evolution Algorithm and Robust to Uncertainty Communication Demand of the ApplicationProceedings of the 2012 12th International Conference on Computational Science and Its Applications10.1109/ICCSA.2012.11(1-6)Online publication date: 18-Jun-2012
  • (2012)Flexible service selection with user-specific QoS support in service-oriented architectureJournal of Network and Computer Applications10.1016/j.jnca.2011.03.01335:3(962-973)Online publication date: 1-May-2012
  • (2012)On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy SchedulersInternational Journal of Approximate Reasoning10.1016/j.ijar.2011.10.00553:2(228-247)Online publication date: 1-Feb-2012
  • (2011)Multi-Objective Artificial Bee Colony for scheduling in Grid environments2011 IEEE Symposium on Swarm Intelligence10.1109/SIS.2011.5952560(1-7)Online publication date: Apr-2011
  • (2010)SPSE: A flexible QoS-based service scheduling algorithm for service-oriented Grid2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW)10.1109/IPDPSW.2010.5470920(1-8)Online publication date: Apr-2010
  • (2010)Cost-driven scheduling of grid workflows using Partial Critical Paths2010 11th IEEE/ACM International Conference on Grid Computing10.1109/GRID.2010.5697955(81-88)Online publication date: Oct-2010
  • (2010)An adaptive multisite mapping for computationally intensive grid applicationsFuture Generation Computer Systems10.1016/j.future.2010.02.00926:6(857-867)Online publication date: 1-Jun-2010
  • (2009)An innovative perspective on mapping in gridsProceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems10.1145/1555284.1555289(27-36)Online publication date: 19-Jun-2009
  • Show More Cited By

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