Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

Information Sciences - Tập 192 - Trang 228-243 - 2012
Hongbo Liu1,2,3, Ajith Abraham3,4, Václav Snášel3,4, Seán McLoone5
1School of Information, Dalian Maritime University, 116026 Dalian, China
2School of Computer, Dalian University of Technology, 116023 Dalian, China
3Machine Intelligence Research Labs, Auburn, WA 98071, USA
4Department of Computer Science, VŠB-Technical University of Ostrava,708 33 Ostrava-Poruba, Czech Republic
5Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland

Tài liệu tham khảo

Abraham, 2006, Swarm intelligence: foundations, perspectives and applications, Swarm Intelligent Systems, Studies in Computational Intelligence, 26, 3, 10.1007/978-3-540-33869-7_1

AlRashidi, 2009, A survey of particle swarm optimization applications in electric power systems, IEEE Transactions on Evolutionary Computation, 13, 913, 10.1109/TEVC.2006.880326

Babaoglu, 2010, A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine, Expert Systems with Applications, 37, 3177, 10.1016/j.eswa.2009.09.064

Binkley, 2008, Balancing exploitation and exploration in particle swarm optimization: velocity-based reinitialization, Information and Media Technologies, 3, 103

Boccia, 2007, A fast job scheduling system for a wide range of bioinformatic applications, IEEE Transactions on NanoBioscience, 6, 149, 10.1109/TNB.2007.897474

Boeringer, 2004, Particle swarm optimization versus genetic algorithms for phased array synthesis, IEEE Transactions on Antennas and Propagation, 52, 771, 10.1109/TAP.2004.825102

Brucker, 2007

Chang, 2009, An ant algorithm for balanced job scheduling in grids, Future Generation Computer Systems, 25, 20, 10.1016/j.future.2008.06.004

Chung, 1952, On the application of the Borel–Cantelli lemma, Transactions of the American Mathematical Society, 72, 179, 10.1090/S0002-9947-1952-0045327-5

Chung, 2009, A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling, Engineering Applications of Artificial Intelligence, 22, 1005, 10.1016/j.engappai.2008.11.004

Clerc, 2006

Clerc, 2002, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6, 58, 10.1109/4235.985692

Das, 2008, Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm, Pattern Recognition Letters, 29, 688, 10.1016/j.patrec.2007.12.002

F. Dong, S. Akl, Scheduling Algorithms for Grid Computing: State of the Art and Open Problems, Tech. Rep., School of Computing, Queen’s University, Kingston, Ontario, January 2006.

Eberhart, 1998, Comparison between genetic algorithms and particle swarm optimization, Lecture Notes in Computer Science, 1447, 611, 10.1007/BFb0040812

Engelbrecht, 2005

S. Ghosh, D. Kundu, K. Suresh, S. Das, A. Abraham, B. Panigrahi, V. Snášel, On some properties of the lbest topology in particle swarm optimization, in: Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems, vol. 3, IEEE Computer Society Press, Washington, 2009, pp. 370–375.

Guo, 2001, Global convergence properties of evolution stragtegies, Mathematica Numerica Sinica, 23, 105

Hansen, 2001, Variable neighborhood search: principles and applications, European Journal of Operational Research, 130, 449, 10.1016/S0377-2217(00)00100-4

Hansen, 2010, Variable neighbourhood search: methods and applications, Annals of Operations Research, 175, 367, 10.1007/s10479-009-0657-6

Hansen, 2006, Variable neighborhood search and local branching, Computers and Operations Research, 33, 3034, 10.1016/j.cor.2005.02.033

He, 2005, Improved particle swarm optimization based on self-adaptive escape velocity, Chinese Journal of Software, 16, 2036, 10.1360/jos162036

Hey, 2009, The fourth paradigm: data-intensive scientific discovery, Microsoft Research

Horn, 2001, AI in medicine on its way from knowledge-intensive to data-intensive systems, Artificial Intelligence in Medicine, 23, 5, 10.1016/S0933-3657(01)00072-0

Jiang, 2007, Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm, Information Processing Letters, 102, 8, 10.1016/j.ipl.2006.10.005

Kadirkamanathan, 2006, Stability analysis of the particle dynamics in particle swarm optimizer, IEEE Transactions on Evolutionary Computation, 10, 245, 10.1109/TEVC.2005.857077

Kennedy, 2001

J. Kennedy, R. Mendes, Population structure and particle swarm performance, in: Proceedings of the Evolutionary Computation on Evolutionary Computation, 2002, pp. 1671–1676.

Khoo, 2007, A multi-dimensional scheduling scheme in a grid computing environment, Journal of Parallel and Distributed Computing, 67, 659, 10.1016/j.jpdc.2007.01.008

Lazinica, 2009

Lee, 1999, Data intensive distributed computing: a medical application example, Lecture Notes in Computer Science, 1593, 150, 10.1007/BFb0100576

Liu, 2007, An hybrid fuzzy variable neighborhood particle swarm optimization algorithm for solving quadratic assignment problems, Journal of Universal Computer Science, 13, 1309

Liu, 2007, Chaotic dynamic characteristics in swarm intelligence, Applied Soft Computing, 7, 1019, 10.1016/j.asoc.2006.10.006

Liu, 2009, A multi-swarm approach to multi-objective flexible job-shop scheduling problems, Fundamenta Informaticae, 95, 1, 10.3233/FI-2009-160

Liu, 2006, Particle swarm optimisation from lbest to gbest, Applied Soft Computing Technologies: The Challenge of Complexity, 34, 537, 10.1007/3-540-31662-0_41

Liu, 2007, A complete multiagent framework for robust and adaptable dynamic job shop scheduling, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37, 904, 10.1109/TSMCC.2007.900658

Martínez, 2008, The generalized PSO: a new door to PSO evolution, Journal of Artificial Evolution and Applications, 2008, 1, 10.1155/2008/861275

Mosheiov, 2010, Scheduling with a common due-window: polynomially solvable cases, Information Sciences, 180, 1492, 10.1016/j.ins.2009.11.042

Parsopoulos, 2002, Recent approaches to global optimization problems through particle swarm optimization, Natural Computing, 1, 235, 10.1023/A:1016568309421

Ranganathan, 2002, Decoupling computation and data scheduling in distributed data-intensive applications, 352

Roshanaei, 2009, A variable neighborhood search for job shop scheduling with set-up times to minimize makespan, Future Generation Computer Systems, 25, 654, 10.1016/j.future.2009.01.004

Y. Shi, R. Eberhart, E. Team, I. Kokomo, Fuzzy adaptive particle swarm optimization, in: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, 2001, pp. 101–106.

Song, 2006, Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling, IEEE Transactions on Computers, 55, 703, 10.1109/TC.2006.89

Song, 2005, Trusted P2P transactions with fuzzy reputation aggregation, IEEE Internet Computing, 9, 24, 10.1109/MIC.2005.136

Song, 2005, Security-driven heuristics and a fast genetic algorithm for trusted grid job scheduling, vol. 65, 4

Sundar, 2010, A swarm intelligence approach to the quadratic minimum spanning tree problem, Information Sciences, 180, 3182, 10.1016/j.ins.2010.05.001

Y. Tao, H. Jin, X. Shi, Grid workflow scheduling based on reliability cost, in: Proceedings of the 2nd International Conference on Scalable Information Systems, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, Belgium, Belgium, 2007.

Trelea, 2003, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, 85, 317, 10.1016/S0020-0190(02)00447-7

F. Van den Bergh, An analysis of particle swarm optimizers, Ph.D. thesis, University of Pretoria, Pretoria, South Africa, November 2001.

Van den Bergh, 2006, A study of particle swarm optimization particle trajectories, Information Sciences, 176, 937, 10.1016/j.ins.2005.02.003

Venugopal, 2005, A deadline and budget constrained scheduling algorithm for eScience applications on data grids, Lecture Notes in Computer Science, 3719, 60, 10.1007/11564621_7

Venugopal, 2008, An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids, Journal of Parallel and Distributed Computing, 68, 471, 10.1016/j.jpdc.2007.07.004

Wen, 2011, A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system, Information Sciences, 181, 567, 10.1016/j.ins.2010.10.001

Wu, 2010, An integrated security-aware job scheduling strategy for large-scale computational grids, Future Generation Computer Systems, 26, 198, 10.1016/j.future.2009.08.004

Xie, 2006, Scheduling security-critical real-time applications on clusters, IEEE Transactions on Computers, 55, 864, 10.1109/TC.2006.110

Xie, 2007, Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity, Journal of Parallel and Distributed Computing, 67, 1067, 10.1016/j.jpdc.2007.06.004

Xie, 2007, Security-driven scheduling for data-intensive applications on grids, Cluster Computing, 10, 145, 10.1007/s10586-007-0015-x

Yu, 2009, Algorithms for divisible load scheduling of data-intensive applications, Journal of Grid Computing, 1

Zhan, 2009, Adaptive particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39, 1362, 10.1109/TSMCB.2009.2015956

Zhang, 2010, A hybrid immune simulated annealing algorithm for the job shop scheduling problem, Applied Soft Computing, 10, 79, 10.1016/j.asoc.2009.06.008

Zhao, 2010, A perturbed particle swarm algorithm for numerical optimization, Applied Soft Computing, 10, 119, 10.1016/j.asoc.2009.06.010

Zhong, 2005, Building a data-mining grid for multiple human brain data analysis, Computational Intelligence, 21, 177, 10.1111/j.0824-7935.2005.00270.x

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Information Sciences

Tập 192

228-243

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