Abstract
Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The task-resource management is the key role in cloud computing systems. Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Task-resource scheduling problem is NP-complete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the task-resource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.
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J. Arshad, P. Townend, J. Xu. An automatic intrusion diagnosis approach for clouds. International Journal of Automation and Computing, vol. 8, no. 3, pp. 286–296, 2011.
Y. K. Guo, L. Guo. IC cloud: Enabling compositional cloud. International Journal of Automation and Computing, vol.8, no. 3, pp. 269–279, 2011.
B. Li, B. Q. Cao, K. M. Wen, R. X. Li. Trustworthy assurance of service interoperation in cloud environment. International Journal of Automation and Computing, vol.8, no. 3, pp. 297–308, 2011.
Y. C. Liu, Y. T. Ma, H. S. Zhang, D. Y. Li, G. S. Chen. A method for trust management in cloud computing: Data coloring by cloud watermarking. International Journal of Automation and Computing, vol. 8, no. 3, pp. 280–285, 2011.
M. Armbrust, A. Fox, R. Grifth, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia. Above the clouds: A berkeley view of cloud computing. Department of Electrical Engineering and Computer Sciences, Technical report, University of California at Berkeley, USA, 2009.
R. Buyya, S. Pandey, C. Vecchiola. Cloudbus toolkit for market-oriented cloud computing. In Proceedings of the 1st International Conference on Cloud Computing, ACM, Berlin, Germany, pp. 24–44, 2009.
S. Pandey, W. Voorsluys, M. Rahman, R. Buyya, J. E. Dobson, K. Chiu. A grid workflow environment for brain imaging analysis on distributed systems. Concurrency and Computation: Practice & Experience, vol. 21, no. 16, pp. 2118–2139, 2009.
Amazon web services, [Online], Available: http://aws.amazon.com, February 25, 2011.
GoGrid home page, [Online], Available: http://www.gogrid.com, February 25, 2011.
J. D. Ullman. Np-complete scheduling problems. Journal of Computer and System Sciences, vol. 10, no. 3, pp. 384–393, 1975.
A. Abraham, R. Buyya, B. Nath. Nature’s heuristics for scheduling jobs on computational grids. In Proceedings of the 8th IEEE International Conference on Advanced Computing and Communications, IEEE, Piscataway, USA, pp. 45–52, 2000.
M. Aggarwal, R. D. Kent, A. Ngom. Genetic algorithm based scheduler for computational grids. In Proceedings of the 19th Annual International Symposium on High Performance Computing Systems and Application, IEEE, Piscataway, USA, pp. 209–215, 2005.
K. Amin, G. von Laszewski, M. Hategan, N. J. Zaluzec, S. Hampton, A. Rossi. GridAnt: A client-controllable grid workflow system. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences, IEEE, Piscataway, USA, pp. 5–8, 2004.
T. Braun, H. Siegel, N. Beck, L. Boloni, M. Maheswaran, A. Reuther, J. Robertson, M. Theys, B. Yao, D. Hensgen, R. Freund. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, vol. 61, no. 6, pp. 810–837, 2001.
J. Cao, S. A. Jarvis, S. Saini, G. R. Nudd. Gridflow: Work-flow management for grid computing. In Proceedings of the 3rd International Symposium on Cluster Computing and the Grid, IEEE, Piscataway, USA, pp. 198–205, 2003.
E. Deelman, G. Singh, M. H. Su, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, K. Vahi, G. B. Berriman, J. Good, A. Laity, J. C. Jacob, D. S. Katz. Pegasus: A framework for mapping complex scientific workflows onto distributed systems. Scientific Programming, vol. 13, no. 3, pp. 219–237, 2005.
N. Furmento, W. Lee, A. Mayer, S. Newhouse, J. Darlington. ICENI: An open grid service architecture implemented with Jini. In Proceedings of the 2002 ACM/IEEE Conference on Supercomputing, IEEE, Piscataway, USA, 2002.
Y. Gao, H. Q. Rong, J. Z. Huang. Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, vol. 21, no. 1, pp. 151–161, 2005.
S. Kim, J. B. Weissman. A genetic algorithm based approach for scheduling decomposable data grid applications. In Proceedings of the 2004 International Conference on Parallel Processing, IEEE, Washington, USA, vol. 1, pp. 406–413, 2004.
Q. Li, Y. Guo. Optimization of resource scheduling in cloud computing. In Proceedings of the 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, IEEE, Timisoara, Romania, pp. 315–320, 2010.
B. Ludäscher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, E. A. Lee, J. Tao, Y. Zhao. Scientific workflow management and the kepler system: Research articles. Concurrency and Computation: Practice & Experience, vol. 18, no. 10, pp. 1039–1065, 2006.
V. D. Martino, M. Mililotti. Sub optimal scheduling in a grid using genetic algorithms. Parallel Computing, vol. 30, no. 5–6, pp. 553–565, 2004.
T. Oinn, M. Addis, J. Ferris, D. Marvin, M. Senger, M. Greenwood, T. Carver, K. Glover, M. R. Pocock, A. Wipat, P. Li. Taverna: A tool for the composition and enactment of bioinformatics workflows. Bioinformatics, vol. 20, no. 17, pp. 3045–3054, 2004.
J. E. Orosz, S. H. Jacobson. Analysis of static simulated annealing algorithm. Journal of Optimization Theory and Applications, vol. 115, no. 1, pp. 165–182, 2002.
S. Pandey, L. Wu, S. M. Guru, R. Buyya. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, IEEE, Perth, Australia, pp. 400–407, 2010.
A. Salman. Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, vol. 26, no. 8. pp. 363–371, 2002.
S. Song, Y. Kwok, K. Hwang. Security-driven heuristics and a fast genetic algorithm for trusted grid job scheduling. In Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, IEEE, Piscataway, USA, pp. 65–74, 2005.
M. F. Tasgetiren, Y. C. Liang, M. Sevkli, G. Gencyilmaz. A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, vol. 177, no. 3, pp. 1930–1947, 2007.
I. Taylor, I. Wang, M. Shields, S. Majithia. Distributed computing with Triana on the grid: Research articles. Concurrency and Computation: Practice & Experience, vol. 17, no. 9, pp. 1197–1214, 2005.
E. Triki, Y. Collette, P. Siarry. A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. European Journal of Operational Research, vol. 166, no. 1, pp. 77–92, 2005.
L. Wang, H. J. Siegel, V. P. Roychowdhury, A. A. Maciejewski. Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. Journal of Parallel and Distributed Computing, vol. 47, no. 1, pp. 8–22, 1997.
M. Wieczored, R. Prodan, T. Fahringer. Scheduling of scientific workflows in the ASKALON grid environment. ACM SIGMOD Record, vol. 34, no. 3, pp. 56–62, 2005.
J. Yu, R. Buyya, K. Ramamohanarao. Workflow scheduling algorithms for grid computing. Metaheuristics for Scheduling in Distributed Computing Environments, F. Xhafa, A. Abraham, Eds., Berlin, Germany: Springer-Verlag, pp. 173–214, 2008.
L. Zhang, Y. Chen, R. Sun, S. Jing, B. Yang. A task scheduling algorithm based on PSO for grid computing. International Journal of Computational Intelligence Research, vol. 4, no. 1, pp. 37–43, 2008.
C. Zhao, S. Zhang, Q. Liu, J. Xie, J. Hu. Independent tasks scheduling based on genetic algorithm in cloud computing. In Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, Beijing, China, pp. 1–4, 2009.
J. Gu, P. Purdom, J. Franco, B. Wah. Algorithms for the satisfiability (SAT) problem: A survey. Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, D. Johnson, M. Trick, Eds., Providence, USA: American Mathematical Society, pp. 19–152, 1997.
C. Bessiere, E. Hebrard, T. Walsh. Local consistencies in SAT. Theory and Applications of Satisfiability Testing, E. Giunchiglia, A. Tacchella, Eds., Berlin, Germany: Springer-Verlag, pp. 400–407, 2003.
M. Davis, G. Logemann, D. Loveland. A machine program for theorem proving. Communications of the ACM, vol.5, no. 7, pp. 394–397, 1962.
A. Frisch, T. J. Peugniez. Solving non-boolean satisfiability problems with stochastic local search. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, ACM, Seattle, USA, vol. 1, pp. 282–288, 2001.
A. Frisch, T. Peugniez, A. Doggett, P. Nightingale. Solving non-boolean satisfiability problems with stochastic local search: A comparison of encodings. Journal of Automated Reasoning, vol. 35, no. 1–3, pp. 143–179, 2005.
K. Iwama, S. Miyazaki. SAR-variable complexity of hard combinatorial problems. IFIP Transactions A: Computer Science and Technology, vol. 1, no. 1, pp. 253–258, 1994.
M. Büttner, J. Rintanen. Improving parallel planning with constraints on the number of operators. In Proceedings of the 15th International Conference on Automated Planning and Scheduling, Monterey, USA, pp. 292–299, 2005.
M. Ernst, T. Millstein, D. Weld. Automatic SATcompilation of planning problems. In Proceedings of the 15th International Joint Conference on Artificial Intelligence, CiteuLike, Nagoya, Japan, pp. 1169–1176, 1997.
H. Kautz. SATPLAN04: Planning as satisfiability. In Proceedings of the 4th International Planning Competition at the 14th International Conference on Automated Planning and Scheduling, Whistler, Canada, pp. 44–45, 2004.
F. Aloul, B. Al-Rawi, A. Al-Farra, B. Al-Roh. Solving employee timetabling problems using Boolean satisfiability. In Proceedings of the IEEE Innovations in Information Technology Conference, IEEE, Dubai, pp. 1–5, 2006.
J. M. Crawford, A. B. Baker. Experimental results on the application of satisfiability algorithms to scheduling problems. In Proceedings of the 12th National Conference on Artificial Intelligence, ACM, Menlo Park, USA, vol.2, pp. 1092–1097, 1994.
M. A. Cruz-Chávez, R. Rivera-Lopez. A local search algorithm for a SAT representation of scheduling problems. In Proceedings of the 2007 International Conference on Computational Science and Its Applications, ACM, Berlin, Germany, pp. 697–709, 2007.
S. O. Memik, F. Fallah. Accelerated SAT-based scheduling of control/data flow graphs. In Proceedings of the 2002 IEEE International Conference on Computer Design: VLSI in Computers and Processors, IEEE, Freiburg, Germany, pp. 395–400, 2002.
A. Wasfy, F. Aloul. Solving the university class scheduling problem using advanced ILP techniques. In Proceedings of the 4th IEEE GCC Conference, IEEE, Piscataway, USA, pp. 1–5, 2007.
H. H. Hoos. SAT-encodings, search space structure, and local search performance. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, ACM, Stockholm, Sweden, vol. 1, pp. 296–302, 1999.
A. Gorbenko, M. Mornev, V. Popov. Planning a typical working day for indoor service robots. IAENG International Journal of Computer Science, vol. 38, no. 3, pp. 176–182, 2011.
A. Gorbenko, M. Mornev, V. Popov, A. Sheka. The problem of sensor placement for triangulation-based localisation. International Journal of Automation and Control, vol.5, no. 3, pp. 245–253, 2011.
A. Gorbenko, V. Popov. On the problem of placement of visual landmarks. Applied Mathematical Sciences, vol.6, no. 14, pp. 689–696, 2012.
A. Gorbenko, V. Popov, A. Sheka. Localization on discrete grid graphs. Computer, Informatics, Cybernetics and Applications, X. He, E. Hua, Y. Lin, X. Liu, Eds., Berlin, Germany: Springer-Verlag, pp. 971–978, 2012.
A toolbox for Matlab TORSCHE Scheduling, [Online], Available: http://rtime.felk.cvut.cz/schedulingtoolbox/manual/, February 26, 2012.
M. Kutil, P. Sucha, R. Capek, Z. Hanzalek. Optimization and scheduling toolbox. Matlab — Modelling, Programming and Simulations, E. P. Leite, Ed., Rijeka, Croatia: Sciyo, pp. 239–260, 2010.
J. Blazewicz, J. K. Lenstra, A. H. G. Rinnooy Kan. Scheduling subject to resource constraints: Classification and complexity. Discrete Applied Mathematics, vol. 5, no. 1, pp. 11–24, 1983.
R. L. Graham, E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan. Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics, vol. 5, no. 2, pp. 287–326, 1979.
zChaff SAT solver, [Online], Available: http://www.princeton.edu/~chaff/zchaff.html, February 26, 2012.
Amazon CloudFront, [Online], Available: http://aws.amazon.com/cloudfront/, February 26, 2012.
Amazon Elastic Compute Cloud (Amazon EC2), [Online], Available: http://aws.amazon.com/ec2/, February 26, 2012.
Web page “Computational resources of IMM UB RAS”, [Online], Available: http://parallel.imm.uran.ru/mvcnow/hardware/supercomp.htm, February 26, 2012. (In Russian)
A. Gorbenko, A. Lutov, M. Mornev, V. Popov. Algebras of stepping motor programs. Applied Mathematical Sciences, vol. 5, no. 34, pp. 1679–1692, 2011.
A. Gorbenko, V. Popov. Self-learning algorithm for visual recognition and object categorization for autonomous mobile robots. Computer, Informatics, Cybernetics and Applications, X. He, E. Hua, Y. Lin, X. Liu, Eds., Berlin, Germany: Springer-Verlag, pp. 1289–1295, 2012.
A. Gorbenko, V. Popov, A. Sheka. Robot self-awareness: Exploration of internal states. Applied Mathematical Sciences, vol. 6, no. 14, pp. 675–688, 2012.
A. Gorbenko, V. Popov, A. Sheka. Robot self-awareness: Temporal relation based data mining. Engineering Letters, vol. 19, no. 3, pp. 169–178, 2011.
SATLIB — The Satisfiability Library, [Online], Available: http://people.cs.ubc.ca/~hoos/SATLIB/index-ubc.html, February 26, 2012.
F. Lardeux, F. Saubion, J. K. Hao. GASAT: A genetic local search algorithm for the satisfiability problem. Evolutionary Computation, vol. 14, no. 2, pp. 223–253, 2006.
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The work was partially supported by Analytical Departmental Program “Developing the Scientific Potential of Higher School” (Nos. 2.1.1/14055 and 2.1.1/13995).
Anna Gorbenko received B. Sc. degree on computer science in Department of Mathematics and Mechanics, Ural State University, Russian Federation in 2009. Currently, she is a researcher of the Department of Intelligent Systems and Robotics of Ural State University. She has (co-)authored 2 books and 17 papers, 10 conferences publications. She received Microsoft Best Paper Award from international conference SYRCoSE 2011.
Her research interests include different aspects of artificial intelligence and robotics.
Vladimir Popov received M. Sc. degree of mathematics in Department of Mathematics and Mechanics, Ural State University, Russian Federation in 1992. From 1996 to 2002, he was a Ph.D. candidate in physical and mathematical sciences in Mathematics and Mechanics Institute of Ural Branch of Russian Academy of Sciences. Since 2002, he is a professor of Ural State University. From 2006 to 2009, he was the chair of the Laboratory of Distributed Computing and Investigation of Models, Algorithms and Programs of Ural State University. Since 2009, he has been the chair of the Department of Intelligent Systems and Robotics of Ural State University. He has (co-)authored 18 books and more than 120 papers, more than 40 conferences publications. He received Microsoft Best Paper Award from international conference in 2011. In 2008, one of his paper won the Russian competitive selection of survey and analytical papers.
His research interests include different aspects of artificial intelligence and robotics.
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Gorbenko, A., Popov, V. Task-resource scheduling problem. Int. J. Autom. Comput. 9, 429–441 (2012). https://doi.org/10.1007/s11633-012-0664-y
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DOI: https://doi.org/10.1007/s11633-012-0664-y