Abstract
The sensitivity analysis of a Cellular Genetic Algorithm (CGA) with local search is used to design a new and faster heuristic for the problem of mapping independent tasks to a distributed system (such as a computer cluster or grid) in order to minimize makespan (the time when the last task finishes). The proposed heuristic improves the previously known Min-Min heuristic. Moreover, the heuristic finds mappings of similar quality to the original CGA but in a significantly reduced runtime (1,000 faster). The proposed heuristic is evaluated across twelve different classes of scheduling instances. In addition, a proof of the energy-efficiency of the algorithm is provided. This convergence study suggests how additional energy reduction can be achieved by inserting low power computing nodes to the distributed computer system. Simulation results show that this approach reduces both energy consumption and makespan.
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References
Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Operations Research/Computer Science Interfaces. Springer, Heidelberg (2008)
Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous. J. Sci. Eng. 3, 195–207 (2000). Special 50th Anniversary Issue
Android 2.2: Javascript Performance. http://www.androidauthority.com/index.php/2010/07/08/android-2-2-cleans-up-with-ios4-in-javascript-performance/
Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hengsen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)
Brucker, P.: Scheduling Algorithms. Springer, Berlin (2004)
Calhoun, D.: Iphone 4 Sunspider Test Results http://davidbcalhoun.com/2010/iphone-4-sunspider-test-results/
Casanova, H., Legrand, A., Zagorodnov, D., Berman, F.: Heuristics for scheduling parameter sweep applications in grid environments. In: Heterogeneous Computing Workshop, pp. 349–363 (2000)
Casanova, H., Zagorodnov, D., Berman, F., Legrand, A.: Heuristics for scheduling parameter sweep applications in grid environments. In: Proceedings of the 9th Heterogeneous Computing Workshop, HCW ’00, pp. 349–363. IEEE Computer Society, Washington (2000)
Cockcroft, A.N.: Millicomputing: the coolest computers and the flashiest storage. In: Int. CMG Conference, pp. 407–414. Computer Measurement Group (2007)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic Algorithms and Evolutionary Computation. Kluwer Academic, Dordrecht (2002)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
Diaz, C.O., Guzek, M., Pecero, J.E., Danoy, G., Bouvry, P., Khan, S.U.: Energy-aware fast scheduling heuristics in heterogeneous computing systems. In: Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Istanbul, Turkey, pp. 478–484 (2011)
Freund, R.F., Gherrity, M., Ambrosius, S., Campbell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., Mirabile, F., Moore, L., Rust, B., Siegel, H.J.: Scheduling resources in multi-user, heterogeneous, computing environments with smartnet. In: Proceedings of the Seventh Heterogeneous Computing Workshop, HCW ’98. IEEE Computer Society, Washington (1998)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)
Ghafoor, A., Yang, J.: Distributed heterogeneous supercomputing management system. IEEE Comput. 26(6), 78–86 (1993)
Ibarra, O.H., Kim, C.E.: Heuristic algorithms for scheduling independent tasks on nonidentical processors. J. ACM 24(2), 280–289 (1977)
IEEE and The Open Group: Posix (IEEE Std 1003.1-2008, Open Group Base Specifications Issue 7). http://www.unix.org
Khan, S.U., Ahmad, I.: Heuristics-based replication schemas for fast information retrieval over the internet. In: ISCA PDCS’04, pp. 278–283 (2004)
Khan, S.U., Ahmad, I.: Comparison and analysis of ten static heuristics-based internet data replication techniques. J. Parallel Distrib. Comput. 68(2), 113–136 (2008)
Kim, J., Siegel, H.J., Maciejewski, A.A., Eigenmann, R.: Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans. Parallel Distrib. Syst. 19(11), 1445–1457 (2008). doi:10.1109/TPDS.2008.113
Li, Y., Liu, Y., Qian, D.: A heuristic energy-aware scheduling algorithm for heterogeneous clusters. In: 2009 15th International Conference on Parallel and Distributed Systems (ICPADS), pp. 407–413. IEEE, Shenzhen (2009). doi:10.1007/s10586-012-0207-x
Luo, P., Lü, K., Shi, Z.: A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 67, 695–714 (2007)
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Proceedings of the Eighth Heterogeneous Computing Workshop, HCW ’99. IEEE Computer Society, Washington (1999)
Microsoft Corporation: Webkit Sunspider http://ie.microsoft.com/testdrive/benchmarks/sunspider/default.html
Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, International Series in Operations Research and Management Science, vol. 57, pp. 105–144. Springer, New York (2003)
Munir, E., Li, J.Z., Shi, S.F., Zou, Z.N., Rasool, Q.: A new heuristic for task scheduling in heterogeneous computing environment. J. Zhejiang Univ. Sci. A 9, 1715–1723 (2008)
Nesmachnow, S., Cancela, H., Alba, E.: Heterogeneous computing scheduling with evolutionary algorithms. Soft computing—A fusion of foundations. Methodol. Appl. 15, 685–701 (2010)
Pinel, F., Danoy, G., Bouvry, P.: Evolutionary algorithm parameter tuning with sensitivity analysis. In: Bouvry, P., Klopotek, M., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybinski, H. (eds.) Security and Intelligent Information Systems. Lecture Notes in Computer Science, vol. 7053, pp. 204–216. Springer, Berlin (2012)
Pinel, F., Dorronsoro, B., Bouvry, P.: A new parallel asynchronous cellular genetic algorithm for de novo genomic sequencing. In: Proceedings of the IEEE International Conference on Soft Computing and Pattern Recognition (SOCPAR09), pp. 178–183 (2009)
Pinel, F., Dorronsoro, B., Bouvry, P.: A new parallel asynchronous cellular genetic algorithm for scheduling in grids. In: Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010, p. 206b (2010)
Pinel, F., Pecero, J., Bouvry, P., Khan, S.: A two-phase heuristic for the scheduling of independent tasks on computational grids. In: International Conference on High Performance Computing and Simulation (HPCS), pp. 471–477 (2011)
Pinel, F., Pecero, J., Khan, U.S., Bouvry, P.: Memory-aware green scheduling on multi-core processors. In: Proceedings of the Second International Workshop on Green Computing, ICPP (2010)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley, New York (2004)
Saltelli, A., Tarantola, S., Chan, K.: A quantitative, model independent method for global sensitivity analysis of model output. Technometrics 41, 39–56 (1999)
Siegel, H.J., Ali, S.: Techniques for mapping tasks to machines in heterogeneous computing systems. J. Syst. Archit. 46, 627–639 (2000)
Xhafa, F.: A hybrid evolutionary heuristic for job scheduling on computational grids. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol. 75, pp. 269–311. Springer, Berlin (2007)
Xhafa, F., Alba, E., Dorronsoro, B., Duran, B.: Efficient batch job scheduling in grids using cellular memetic algorithms. J. Math. Model. Algorithms 7, 217–236 (2008)
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This work is supported by the Fonds National de la Recherche Luxembourg, CORE Project Green-IT (C09 IS/05).
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Pinel, F., Dorronsoro, B., Pecero, J.E. et al. A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster Comput 16, 421–433 (2013). https://doi.org/10.1007/s10586-012-0207-x
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DOI: https://doi.org/10.1007/s10586-012-0207-x