Abstract
Global warming and climate change are threats that the planet is facing nowadays. Green computing has emerged as a challenge to reduce the energy consumption and pollution footprints of computers. Grid Computing could match the principles of Green Computing as it could exploit and efficiently use processors’ computing power. This paper presents a swarm multi-objective optimization algorithm for scheduling experiments (the job execution) on the Grid. Multi-Objective Firefly Algorithm (MO-FA) is inspired by the brightness attraction among fireflies. One of the main contributions of this work is that the increasing firefly brightness is interpreted as an improvement in response time and energy savings. This would fulfill both conflicting objectives of Grid users: execution time and energy consumption. Results show that MO-FA is a reliable method according to its interquartile range and its comparison with the standard and well-known multi-objective algorithm NSGA-II. Moreover, it performs better than actual grid schedulers as the Workload Management System (WMS) and the Deadline Budget Constraint (DBC).
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References
Arsuaga-Ríos, M., Vega-Rodríguez, M.A.: Multi-objective Firefly Algorithm for Energy Optimization in Grid Environments. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 350–351. Springer, Heidelberg (2012)
Bodenstein, C.: Heuristic scheduling in grid environments: Reducing the operational energy demand. In: Neumann, D., Baker, M., Altmann, J., Rana, O. (eds.) Economic Models and Algorithms for Distributed Systems. Autonomic Systems, pp. 239–256. Birkhäuser, Basel (2010)
Buyya, R., Murshed, M., Abramson, D.: A deadline and budget constrained cost-time optimisation algorithm for scheduling task farming applications on global grids. In: Int. Conf. on Parallel and Distributed Processing Techniques and Applications, Las Vegas, Nevada, USA, pp. 2183–2189 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Diaz, C.O., Guzek, M., Pecero, J.E., Bouvry, P., Khan, S.U.: Scalable and energy-efficient scheduling techniques for large-scale systems. In: Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology, CIT 2011, pp. 641–647. IEEE Computer Society, Washington, DC (2011)
Dolz, M.F., Fernández, J.C., Iserte, S., Mayo, R., Quintana, E.S., Cotallo, M.E., Díaz, G.: Energysaving cluster experience in ceta-ciemat. In: Ibergrid (ed.) 5th Iberian Grid Infrastructure Conference, Santander, Spain, pp. 39–50 (2011)
Entezari-Maleki, R., Movaghar, A.: A probabilistic task scheduling method for grid environments. Future Gener. Comput. Syst. 28(3), 513–524 (2012)
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)
Hernández, C.J.B., Sierra, D.A., Varrette, S., Pacheco, D.L.: Energy efficiency on scalable computing architectures. In: Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology, CIT 2011, pp. 635–640. IEEE Computer Society, Washington, DC (2011)
Khan, S.U., Ahmad, I.: A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids. IEEE Trans. Parallel Distrib. Syst. 20(3), 346–360 (2009)
Khan, S.U.: A goal programming approach for the joint optimization of energy consumption and response time in computational grids. In: 28th IEEE International Performance Computing and Communications Conference, pp. 410–417 (2009)
Khan, S.U.: A multi-objective programming approach for resource allocation in data centers. In: The 2009 International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 152–158 (2009)
Khare, V., Yao, X., Deb, K.: Performance Scaling of Multi-objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)
Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374–1381 (2011)
Lee, Y.H., Leu, S., Chang, R.S.: Improving job scheduling algorithms in a grid environment. Future Gener. Comput. Syst. 27(8), 991–998 (2011)
Lindberg, P., Leingang, J., Lysaker, D., Khan, S.U., Li, J.: Comparison and analysis of eight scheduling heuristics for the optimization of energy consumption and makespan in large-scale distributed systems. The Journal of Supercomputing 59(1), 323–360 (2012)
Liu, W., Li, H., Du, W., Shi, F.: Energy-aware task clustering scheduling algorithm for heterogeneous clusters. In: Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications, GREENCOM 2011, pp. 34–37. IEEE Computer Society, Washington, DC (2011)
Lovasz, G., Berl, A., De Meer, H.: Energy-efficient and performance-conserving resource allocation in data centers. In: Proc. of the COST Action IC0804 on Energy Efficiency in Large Scale Distributed Systems - 2nd Year, pp. 31–35. IRIT (2011)
Sulistio, A., Poduval, G., Buyya, R., Tham, C.: On incorporating differentiated levels of network service into gridsim. Future Gener. Comput. Syst. 23(4), 606–615 (2007)
Talukder, A.K.M.K.A., Kirley, M., Buyya, R.: Multiobjective differential evolution for scheduling workflow applications on global grids. Concurr. Comput.: Pract. Exper. 21(13), 1742–1756 (2009)
Tsai, M.-Y., Chiang, P.-F., Chang, Y.-J., Wang, W.-J.: Heuristic Scheduling Strategies for Linear-Dependent and Independent Jobs on Heterogeneous Grids. In: Kim, T.-h., Adeli, H., Cho, H.-s., Gervasi, O., Yau, S.S., Kang, B.-H., Villalba, J.G. (eds.) GDC 2011. CCIS, vol. 261, pp. 496–505. Springer, Heidelberg (2011)
Tsuchiya, T., Osada, T., Kikuno, T.: Genetics-based multiprocessor scheduling using task duplication. Microprocessors and Microsystems 22(3-4), 197–207 (1998)
Wang, L., von Laszewski, G., Dayal, J., Wang, F.: Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID 2010, pp. 368–377. IEEE Computer Society, Washington, DC (2010)
Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)
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Arsuaga-Ríos, M., Vega-Rodríguez, M.A. (2013). A Multi-objective Proposal Based on Firefly Behaviour for Green Scheduling in Grid Systems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_8
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DOI: https://doi.org/10.1007/978-3-642-37213-1_8
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