Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submit...

Full description

Saved in:

Bibliographic Details
Main Author:	Alobaedy, Mustafa Muwafak Theab
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/5382/1/s93630.pdf https://etd.uum.edu.my/5382/7/s93630_abstract.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.5382
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku Mahamud, Ku Ruhana
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Alobaedy, Mustafa Muwafak Theab Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
description	Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Alobaedy, Mustafa Muwafak Theab
author_facet	Alobaedy, Mustafa Muwafak Theab
author_sort	Alobaedy, Mustafa Muwafak Theab
title	Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
title_short	Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
title_full	Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
title_fullStr	Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
title_full_unstemmed	Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
title_sort	hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5382/1/s93630.pdf https://etd.uum.edu.my/5382/7/s93630_abstract.pdf
_version_	1747827922007228416
spelling	my-uum-etd.53822021-03-18T03:46:26Z Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing 2015 Alobaedy, Mustafa Muwafak Theab Ku Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan. 2015 Thesis https://etd.uum.edu.my/5382/ https://etd.uum.edu.my/5382/1/s93630.pdf text eng public https://etd.uum.edu.my/5382/7/s93630_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Aarts, E., Korst, J., & Michiels, W. (2014). Simulated Annealing. In E. K. Burke & G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (pp. 265–285). Boston: Springer. Aarts, E., & Lenstra, J. K. (2003). Local Search in Combinatorial Optimization. Princetonn: Princeton University Press. Abraham, A., Grosan, C., & Ishibuchi, H. (2007). Hybrid Evolutionary Algorithms. Heidelberg: Springer. Abraham, A., Liu, H., Zhang, W., & Chang, T. (2006). Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm. In Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (pp. 500–507). Bournemouth. Agnetis, A., Billaut, J.-C., Gawiejnowicz, S., Pacciarelli, D., & Soukhal, A. (2014). Multiagent Scheduling Models and Algorithms. Heidelberg: Springe. Alba, E., Almeida, F., Blesa, M., Cabeza, J., Cotta, C., Díaz, M., … Xhafa, F. (2002). MALLBA: A Library of Skeletons for Combinatorial Optimisation. In Proceedings of the 8th International Euro-Par Conference on Parallel Processing (pp. 927–932). Paderborn. Alba, E., Almeida, F., Blesa, M., Cotta, C., Diaz, M., Dorta, I., … Xhava, F. (2006). Efficient Parallel LAN / WAN Algorithms for Optimization . The MALLBA Project. Journal of Parallel Computing, 32(5-6), 415–440. Aleti, A. (2012). An Adaptive Approach to Controlling Parameters of Evolutionary Algorithms. (Doctoral dissertation). Retrieved from http://researchbank.swinburne.edu.au/vital/ access/manager/Index AL-Fawair, M. A. (2009). A Framework for Evolving Grid Computing Systems. (Doctoral dissertation). Retrieved from http://www.divaportal.org/smash/search.jsf Ali, S. S., Siegel, H. J., Maheswaran, M., Hensgen, D., & Lafayette, W. (2000). Task Execution Time Modeling for Heterogeneous Computing Systems. In Proceedings of the 9th Heterogeneous Computing Workshop (pp. 185–199). Cancun. doi:10.1109/HCW.2000.843743 Ali, S., Siegel, H. J., Maheswaran, M., Hensgen, D., & Ali, S. (2000). Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems. Tamkang Journal of Science and Engineering, 3(3), 195–207. Anousha, S., Anousha, S., & Ahmadi, M. (2014). A New Heuristic Algorithm for Improving Total Completion Time in Grid Computing. In James J. Park, S.-C. Chen, J.-M. Gil, & N. Y. Yen (Eds.), Multimedia and Ubiquitous Engineering (pp. 17–26). Heidelberg: Springe. Aron, R., & Chana, I. (2012). Formal QoS Policy Based Grid Resource Provisioning Framework. Journal of Grid Computing, 10(2), 249–264. doi:10.1007/s10723-012-9202-y Atasagun, Y., & Kara, Y. (2014). Bacterial Foraging Optimization Algorithm for Assembly Line Balancing. Journal of Neural Computing and Applications, 25(1), 237–250. Babafemi, O., Sanjay, M., & Adigun, M. (2013). Towards Developing Grid-Based Portals for E-Commerce on-Demand Services on a Utility Computing Platform. Journal of Procedia, 4(1), 81–87. doi:10.1016/j.ieri.2013.11.013 Bagherzadeh, J., & MadadyarAdeh, M. (2009). An Improved Ant Algorithm for Grid Scheduling Problem. In Proceedings of the 14th International Conference on CSI Computer (pp. 323–328). Tehran. Bai, J., Chen, L., Jin, H., Chen, R., & Mao, H. (2012). Robot Path Planning Based on Random Expansion of Ant Colony Optimization. In Z. C. Qian, W. Su, T. Wang, & H. Yang (Eds.), Recent Advances in Computer Science and Information Engineering (pp. 141–146). Heidelberg: Springer. Bai, L., Hu, Y., Lao, S., & Zhang, W. (2010). Task scheduling with load balancing using multiple ant colonies optimization in grid computing. 2010 Sixth International Conference on Natural Computation, (Icnc), 2715–2719. doi:10.1109/ICNC.2010.5582599 Bandieramonte, M., Stefano, A. Di, & Morana, G. (2008). An ACO Inspired Strategy to Improve Jobs Scheduling in a Grid Environment. In Proceedings of the 8th International Conference on Algorithms and Architectures for Parallel Processing (pp. 30–41). Cyprus. Bardsiri, A. K., & Hashemi, S. M. (2012). A Comparative Study on Seven Static Mapping Heuristics for Grid Scheduling Problem. International Journal of Software Engineering and Its Applications, 6(4), 247–256. Berman, F., Fox, G., & Hey, A. J. G. (2003). Grid Computing: Making the Global Infrastructure a Reality. Chichester, England: Wiley. Birattari, M. (2009). Tuning Metaheuristics A Machine Learning Perspective. Berlin: Springer. Biswal, B., Dash, P. K., & Mishra, S. (2011). A Hybrid Ant Colony Optimization Technique for Power Signal Pattern Classification. Journal of Expert Systems with Applications, 38(5), 6368–6375. Blum, C., & Li, X. (2008). Swarm Intelligence in Optimization. In C. Blum & D. Merkle (Eds.), Swarm Intelligence. Heidelberg: Springer. Blum, C., & Roli, A. (2003). Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. Journal of ACM Computing Surveys, 35(3), 268–308. doi:10.1145/937503.937505 Botzheim, J., Toda, Y., & Kubota, N. (2012). Bacterial Memetic Algorithm for Simultaneous Optimization of Path Planning and Flow Shop Scheduling Problems. Journal of Artificial Life and Robotics, 17(1), 107–112. Boussaid, I., Lepagnot, J., & Siarry, P. (2013).A Survey on Optimization Metaheuristics. Journal of Information Sciences, 237(1), 82–117. Brade, D., & Lehmann, A. (2002). Model Verification and Validation. In A. N. Ince (Ed.), Modeling and Simulation Environment for Satellite and Terrestrial Communications Networks. Boston: Springer. doi:10.1007/978-1-4615-0863-2_17 Braun, T. D., Siegel, H. J., Beck, N., Boloni, L. L., Maheswaran, M., Reuther, A. I.,… Freund, R. F. (1999). A Comparison Study of Static Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems. In Proceedings of the 8th Heterogeneous Computing Workshop (pp. 15–29). San Juan. Braun, T. D., Siegel, H. J., Beck, N., Boloni, L. L., Maheswaran, M., Reuther, A. I.,… Freund, R. F. (2001). A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing, 61(6), 810–837. doi:10.1006/jpdc.2000.1714 Bullnheimer, B., Hart, R. F., & Straub, C. (1999). A New Rank-Based Version of the Ant System: A Computational Study. Central European Journal of Operations Research and Economics, 7(1), 25 – 38. Burke, E. K., & Kendall, G. (2014). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. New York: Springer. Buyya, R., & Murshed, M. (2002). GridSim: a Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Journal of Concurrency and Computation: Practice and Experience, 14(13), 1175–1220. doi:10.1002/cpe.710 Cai, R., Ning, Z., Li, L., & Zhong, Y. (2007). Simulated Annealing Algorithm for Independent Tasks Assignment in Heterogeneous Computing Systems. In Proceedings of the 3rd International Conference on Natural Computation (pp. 105–109). Haikou. Calvete, H. I., Gale, C., & Oliveros, M. (2012). Ant Colony Optimization for Solving the Vehicle Routing Problem with Delivery Preferences. In Proceedings of the International Conference on Modeling and Simulation in Engineering, Economics and Management (pp. 230–239). New Rochelle. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraula, G., & Bonabeau, E. (2003). Self-Organization in Biological Systems. Princeton, N.J. Oxford: Princeton University Press. Caron, E., Garonne, V., & Tsaregorodtsev, A. (2007). Definition, Modelling and Simulation of a Grid Computing Scheduling System for High Throughput Computing. Journal of Future Generation Computer Systems, 23(8), 968–976. doi:10.1016/j.future.2007.04.008 Carretero, J., & Xhafa, F. (2006). Use of Genetic Algorithms for Scheduling Jobs in Large Scale Grid Applications. Journal of Technological and Economic Development of Economy, 12(1), 11–17. Carretero, J., Xhafa, F., & Abraham, A. (2007). Genetic Algorithm Based Schedulers for Grid Computing Systems. International Journal of Innovative Computing, Information and Control, 3(6), 1–19. Carvalho, M., & Brasileiro, F. (2012). A User-Based Model of Grid Computing Workloads. In Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (pp. 40–48). Beijing. Chang, R., Chang, J., & Lin, P.-S. (2009). An Ant Algorithm for Balanced Job Scheduling in Grids. Journal of Future Generation Computer Systems, 25(1), 20–27. doi:10.1016/j.future.2008.06.004 Chang, R.-S., Chang, J.-S., & Lin, P.-S. (2007). Balanced Job Assignment Based on Ant Algorithm for Computing Grids. In Proceedings of the 2nd IEEE Asia-Pacific Conference on Service Computing (pp. 291–295). Tsukuba Science City. Chaturvedi, A. K., & Sahu, R. (2011). New Heuristic for Scheduling of Independent Tasks in Computational Grid. International Journal of Grid and Distributed Computing, 4(3). Chen, X., Kong, Y., Fang, X., & Wu, Q. (2011). A Fast Two-Stage ACO Algorithm for Robotic Path Planning. Journal of Neural Computing and Applications, 22(2), 313–319. Chen, Y., Yu, L., Hong, Z., & Dong, Q. (2012). On Energy-Saving Routing Based on Ant Colony Algorithm for Wireless Sensor Networks. In Proceedings of the International Conference in Electrics, Communication and Automatic Control Proceedings (pp. 1241–1247). Chongqing. Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed Optimization by Ant Colonies. In Proceedings of the European Conference on Artificial Life (pp. 134 – 142). Paris. Conejero, J., Caminero, B., Carrion, C., & Tomas, L. (2014). From Volunteer to Trustable Computing: Providing QoS-Aware Scheduling Mechanisms for Multi-Grid Computing Environments. Journal of Future Generation Computer Systems, 34(1), 76–93. doi:10.1016/j.future.2013.12.005 Cordon, O., Viana, I. F. de, & Herrera, F. (2002). Analysis of the Best-Worst Ant System and its Variants on the TSP. Journal of Mathware and Soft Computing, 9(3), 228–234. Cordon, O., Viana, I. F. de, Herrera, F., & Moreno, L. (2000). A New ACO Model Integrating Evolutionary Computation Concept: The Best-Worst Ant System. In Proceedings of the 3rd International Workshop on Ant Algorithms (pp. 22–29). Granada. Costa, D. (1994). An Evolutionary Tabu Search Algorithm and the NHL Scheduling Problem. Journal of INFOR, 33(1), 161–178. Cyril Daisy Christina, P., & Miriam, D. D. H. (2012). Adaptive Task Scheduling Based on Multi Criterion Ant Colony Optimization in Computational Grids. In Proceedings of the International Conference on Recent Trends in Information Technology (pp. 185–190). Tamil Nadu. David, N. (2013). Validating Simulations. In B. Edmonds & R. Meyer (Eds.), Simulating Social Complexity. Berlin: Springer. doi: 10.1007/978-3-540-93813-2_8 Davis, L. D. (1991). Handbook Of Genetic Algorithms. New York: Van Nostrand Reinhold Computer Library. Devi, S. N., & Pethalakshmi, A. (2012). Resource Discovery for Grid Computing Environment Using Ant Colony Optimization by Applying Routing Information and LRU Policy. In Proceedings of the 4th International Conference on Global Trends in Computing and Communication Systems (pp. 124–133). Vellore, India. Do Duc, D., Dinh, H. Q., Dang, T. H., Laukens, K., & Hoang, X. H. (2012). AcoSeeD: An Ant Colony Optimization for Finding Optimal Spaced Seeds in Biological Sequence Search. In Proceedings of the 8th International Conference on Swarm Intelligence (pp. 204–211). Brussels. Dokeroglu, T., & Cosar, A. (2012). Dynamic Programming with Ant Colony Optimization Metaheuristic for Optimization of Distributed Database Queries. In Proceedings of the 26th International Symposium on Computer and Information Sciences (pp. 107–113). London. Dorigo, M., & Gambardella, L. M. (1997a). Ant Colonies for the Travelling Salesman Problem. Journal of BioSystems, 43(2), 73–81. doi:10.1016/S0303-2647(97)01708-5 Dorigo, M., & Gambardella, L. M. (1997b). Ant Colony System: a Cooperative Learning Approach to the Traveling Salesman Problem. Journal of IEEE Transactions on Evolutionary Computation, 1(1), 53–66. doi:10.1109/4235.585892 Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive Feedback as a Search Strategy (Report No 91- 016) (pp. 1–20). Milan. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a Colony of Cooperating Agents. Journal of IEEE Transactions on Systems, Man, and Cybernetics-Part B, Cybernetics, 26(1), 29–41. doi:10.1109/3477.484436 Dorigo, M., & Stutzle, T. (2004). Ant Colony Optimization. Cambridge, Mass: MIT Press. Dorigo, M., Stutzle, T., & Stützle, T. (2003). The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. In F. Glover & G. A. Kochenberger (Eds.), Handbook of Metaheuristics (pp. 250–285). Boston: Kluwer Academic Publishers. Douiri, S. M., & Elbernoussi, S. (2012). A New Ant Colony Optimization Algorithm for the Lower Bound of Sum Coloring Problem. Journal of Mathematical Modelling and Algorithms, 11(2), 181–192. Dumitrescu, C. L., & Foster, I. (2005). GangSim: a Simulator for Grid Scheduling Studies. In Proceedings of the IEEE International Symposium on Cluster Computing and the Grid (pp. 1151–1158). Cardiff. Eberhart, R., & Kennedy, J. (1995). A New Optimizer Using Particle Swarm Theory. In Proceddings of the 6th International Symposium on Micromachine and Human Science (pp. 39–43). Nagoya. Espling, D. (2013). Enabling Technologies for Management of Distributed Computing Infrastructures. (Doctoral dissertation). Retrieved from http://www.divaportal.org/smash/ search.jsf Farina, A., Graziano, A., Panzieri, S., Pascucci, F., & Setola, R. (2013). How to Perform Verification and Validation of Critical Infrastructure Modeling Tools. In Proceedings of the 6th International Workshop on Critical Information Infrastructure Security (pp. 116– 127). Lucerne. doi:10.1007/978-3-642-41476-3_10 Feitelson, D. G. (2013). Workload Modeling for Computer Systems Performance Evaluation. Jerusalem: The Hebrew University of Jerusalem. Fidanova, S. (2006). Simulated Annealing for Grid Scheduling Problem. In Proceedings of the International Symposium on Modern Computing (pp. 41–45). Sofia. Fidanova, S., & Durchova, M. (2006). Ant Algorithm for Grid Scheduling Problem. In Proceedings of the 5th International Conference on Large-Scale Scientific Computing (pp. 405–412). Sozopol. Fogel, L. J., Owens, A. J., & Walsh, M. J. (1996). Artificial Intelligence Through Simulated Evolution. New York: Wiley. Foster, I., & Kesselman, C. (1997). Globus: a Metacomputing Infrastructure Toolkit. International Journal of High Performance Computing Applications, 11(2), 115–128. Foster, I., & Kesselman, C. (2004). The Grid 2, Second Edition: Blueprint for a New Computing Infrastructure. Amsterdam Boston: Morgan Kaufmann. Framinan, J. M., Leisten, R., & García, R. R. (2014). Manufacturing Scheduling Systems An Integrated View on Models, Methods and Tools. London: Springer. Fulop, N. Z. C. (2008). A Desktop Grid Computing Approach for Scientific Computing and Visualization. (Doctoral dissertation). Retrieved from http://www.diva-portal.org/smash/ search.jsf Gainaru, A., Cappello, F., Trausan-matu, S., & Kramer, B. (2011). Event Log Mining Tool for Large Scale HPC Systems. In Proceedings of the 17th International Euro-Par Conference on Parallel Processing (pp. 52–64). Bordeaux, France. Gambardella, L. M. L. M., Montemanni, R., & Weyland, D. (2012). An Enhanced Ant Colony System for the Sequential Ordering Problem. Proceedings of the International Conference on Operations Research, 355–360. doi:10.1007/978-3- 642- Garrido, J. M. (2001). Object-Oriented Discrete-Event Simulation with Java: A Practical Introduction. New York: Kluwer Academic. doi:10.1007/978-1-4615-1319-3 Gauci, M., Dodd, T. J., & Groß, R. (2012). Why “GSA: a Gravitational Search Algorithm” is not Genuinely Based on the Law of Gravity. Journal of Natural Computing, 11(4), 719–720. doi:10.1007/s11047-012-9322-0 Gazi, V., & Passino, K. M. (2011). Swarm Stability and Optimization. Heidelberg: Springer. Ge, H., & Tan, G. (2012). A Cooperative Intelligent Approach for Job-shop Scheduling Based on Bacterial Foraging Strategy and Particle Swarm Optimization. In C. Kahraman (Ed.), Computational Intelligence Systems in Industrial Engineering (pp. 363–383). Paris: Atlantis Press. Gen, M., Zhang, W., Lin, L., & Jo, J. (2014). Recent Advances in Multiobjective Genetic Algorithms for Manufacturing Scheduling Problems. In Proceedings of the 8th International Conference on Management Science and Engineering Management (pp. 815–831). Lisbon. Gendreau, M., & Potvin, J. (2014). Tabu Search. In E. K. Burke & G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (pp. 243–263). Boston, MA: Springer. Gendreau, M., & Potvin, J.-Y. (2010). Handbook of Metaheuristics. New York: Springer. Glover, F. (1986). Future Paths for Integer Programming and Links to Artificial Intelligence. Journal of Computers and Operations Research, 13(5), 533 – 549. doi:10.1016/0305-0548(86)90048-1 Glover, F., & Laguna, M. (1997). Tabu Search. Boston: Kluwer Academic. Golshanara, L., Rankoohi, S. M. T. R., & Shah-Hosseini, H. (2013). A Multi-Colony Ant Algorithm for Optimizing Join Queries in Distributed Database Systems. Journal of Knowledge and Information Systems, 39(1), 75–206. Grosan, C., & Abraham, A. (2007). Hybrid Evolutionary Algorithms : Methodologies, Architectures , and Reviews. In A. Abraham, C. Grosan, & H. Ishibuchi (Eds.), Hybrid Evolutionary Algorithms (pp. 1–17). Heidelberg: Springer. Guo, Y., & Wang, X. (2010). Application of Simulated Annealing Algorithm toGrid Computing Scheduling based on GridSim. In Proceedings of the 2nd International Conference on Information Science and Engineering (pp. 1021–1024). Hangzhou, China: Ieee. doi:10.1109/ICISE.2010.5691633 Hamid, Z. A., Musirin, I., Rahim, M. N. A., & Kamari, N. A. M. (2012). Application of Electricity Tracing Theory and Hybrid Ant Colony Algorithm for Ranking Bus Priority in Power System. International Journal of Electrical Power & Energy Systems, 43(1), 1427–1434. Hao, Y., Liu, G., & Wen, N. (2012). An Enhanced Load Balancing Mechanism Based on Deadline Control on GridSim. Journal of Future Generation Computer Systems, 28(4), 657–665. doi:10.1016/j.future.2011.10.010 Harchol-Balter, M., Crovella, M. E., & Murta, C. D. (1999). On Choosing a Task Assignment Policy for a Distributed Server System. Journal of Parallel and Distributed Computing, 59(2), 204–228. Hartmann, J., Makuschewitz, T., Frazzon, E. M., & Scholz-Reiter, B. (2014). A Genetic Algorithm for the Integrated Scheduling of Production and Transport Systems. In Proceedings of of the International Annual Conference of the German Operations Research Society (pp. 533–539). Leibniz University of Hannover, Germany. Heien, E., Kondo, D., Gainaru, A., LaPine, D., Kramer, B., & Cappello, F. (2011). Modeling and Tolerating Heterogeneous Failures in Large Parallel Systems. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (pp. 1–11). Seatle, WA. Herd, B., Miles, S., McBurney, P., & Luck, M. (2014). Verification and Validation of Agent-Based Simulations Using Approximate Model Checking. In Proceedings of the International Workshop on Multi-Agent-Based Simulation XIV (pp. 53–70). Saint Paul. doi:10.1007/978-3-642-54783-6 4, Hodnefjell, S., & Junior, I. C. (2012). Classification Rule Discovery with Ant Colony Optimization Algorithm. In Proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning (pp. 678–687). Natal, Brazil. Hogenboom, A., Frasincar, F., & Kaymak, U. (2013). Ant Colony Optimization for RDF Chain Queries for Decision Support. Journal of Expert Systems with Applications, 40(5), 1555–1563. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. Cambridge, Mass: MIT Press. Hussain, H., Malik, S. U. R., Hameedb, A., Khanb, S. U., Bickler, G., Min-Allah, N., … Rayes, A. (2013). A Survey on Resource Allocation in High Performance Distributed Computing Systems. Journal of Parallel Computing, 39(11), 709–736. Iosup, A. A., Epema, D. J. H. J., Maassen, J., & Nieuwpoort, R. van. (2007). Synthetic Grid Workloads with Ibis, Koala, and Grenchmark. In S. Gorlatch & M. Danelutto (Eds.), Integrated Research in GRID Computing SE - 20 (pp. 271–283). Pisa, Italy: Springer US. Iosup, A., Jan, M., Sonmez, O., & Epema, D. H. J. (2007). On the Dynamic Resource Availability in Grids. In Proceedings of the 8th IEEE/ACM International Conference on Grid Computing (pp. 26–33). Austin, Texas. Izakian, H., Abraham, A., & Snasel, V. (2009a). Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems. Journal of Sensors, 9(7), 5339–50. Izakian, H., Abraham, A., & Snasel, V. (2009b). Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems: A Meta-Heuristic Particle Swarm Optimization Approach. In Proceedings of the 9th International Conference on Hybrid Intelligent Systems (pp. 397–402). Shenyang. Izakian, H., Abraham, A., & Snsel, V. (2009). Performance Comparison of Six Efficient Pure Heuristics for Scheduling Meta-Tasks on Heterogeneous Distributed Environments. Journal of Neural Network World, 6(09), 695–711. Izakian, H., Ladani, B. T., Abraham, A., & Snasel, V. (2010). A Discrete Particle Swarm Optimization Approach for Grid Job Scheduling. International Journal of Innovative Computing, Information and Control, 6(9), 1–15. Izakian, H., Ladani, B. T., Zamanifar, K., & Abraham, A. (2009). A Novel Particle Swarm Optimization Approach for Grid Job Scheduling. In Proceedings of the 3rd International Conference on Information Systems, Technology and Management (pp. 100–109). Ghaziabad. doi:10.1007/978-3-642-00405-6_14 Javadi, B., Kondo, D., Iosup, A., & Epema, D. (2013). The Failure Trace Archive: Enabling the Comparison of Failure Measurements and Models of Distributed Systems. Journal of Parallel and Distributed Computing, 73(8), 1208–1223. Javadi, B., Kondo, D., Vincent, J., & Anderson, D. P. (2009). Mining for Statistical Models of Availability in Large-Scale Distributed Systems: An Empirical Study of SETI @ Home. In Proceedings of the IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (pp. 1–10). London. Jia, Q., & Seo, Y. (2013). An Improved Particle Swarm Optimization for the Resource-Constrained Project Scheduling Problem. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2627–2638. Jourdan, L., Basseur, M., & Talbi, E.-G. (2009). Hybridizing Exact Methods and Metaheuristics: A Taxonomy. European Journal of Operational Research, 199(3), 620–629. doi:10.1016/j.ejor.2007.07.035 Kant, A., Sharma, A., Agarwal, S., & Chandra, S. (2010). An ACO Approach to Job Scheduling in Grid Environment. In Proceedings of the 1st International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 286– 295). Chennai. doi:10.1007/978-3-642-17563-3_35 Karaboga, D. (2005). An Idea Based On Honey Bee Swarm For Numerical Optimization (Technical Report No TR06). Turkey: Erciyes University. Karaboga, D., & Basturk, B. (2008). On the Performance of Artificial Bee Colony (ABC) Algorithm. Journal of Applied Soft Computing, 8(1), 687–697. Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks (pp. 1942–1948). Perth. Kim, J.-K., Shivle, S., Siegel, H. J., Maciejewski, A., Braun, T. D., Schneider, M., … SankarYellampalli, S. (2007). Dynamically Mapping Tasks with Priorities and Multiple Deadlines in a Heterogeneous Environment. Journal of Parallel and Distributed Computing, 67(1), 154–169. Kim, S.-S., Byeon, J.-H., Liu, H., Abraham, A., & McLoone, S. (2013). Optimal Job Scheduling in Grid Computing using Efficient Binary Artificial Bee Colony Optimization. Journal of Soft Computing, 17(5), 867–882. doi:10.1007/s00500-012-0957-7 Klusacek, D., & Rudova, H. (2010). The Importance of Complete Data Sets for Job Scheduling Simulations. In Proceedings of the 15th International Workshop on Job Scheduling Strategies for Parallel Processing (pp. 132– 153). Atlanta. doi:10.1007/978-3-642-16505-4_8 Kokilavani, T., & Amalarethinam, D. I. G. (2013). An Ant Colony Optimization Based Load Sharing Technique for Meta Task Scheduling in Grid Computing. In Proceedings of the 2nd International Conference on Advances in Computing and Information Technology (pp. 395 –404). Chennai. doi:10.1007/978-3-642-31552-7_41 Kolasa, T., & Krol, D. (2010). ACO-GA Approach to Paper-Reviewer Assignment Problem in CMS. In Proceedings of the International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 360–369). Gdynia, Poland. Kolodziej, J. (2012). Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems. New York: Springer. doi:10.1007/978-3-642-28971-2 Kołodziej, J., & Khan, S. U. (2012). Multi-Level Hierarchic Genetic-Based Scheduling of Independent Jobs in Dynamic Heterogeneous Grid Environment. Journal of Information Sciences, 214(1), 1–19. doi:10.1016/j.ins.2012.05.016 Kolodziej, J., Xhafa, F., Bogdanski, M., & Bogda, M. (2010). Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling. In Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (pp. 526–535). Krakow. Kolodziej, J., Xhafa, F., & Kolanko, L. (2009). Hierarchic Genetic Scheduler of Independent Jobs in Computational Grid Environment. In Proceedings of the 23rd European Conference on Modelling and Simulation (pp. 1–7). Madrid. Kołodziej, J., Xhafa, F., & Kolodziej, J. (2011). Enhancing the Genetic-Based Scheduling in Computational Grids by a Structured Hierarchical Population. Journal of Future Generation Computer Systems, 27(8), 1035–1046. doi:10.1016/j.future.2011.04.011 Kothari, V., Anuradha, J., Shah, S., & Mittal, P. (2012). A Survey on Particle Swarm Optimization in Feature Selection. In Proceedings of the 4th International Conference on Global Trends in Information Systems and Software Applications (pp. 192–201). Vellore. Kousalya, K., & Balasubramanie, P. (2008). An Enhanced Ant Algorithm for Grid Scheduling Problem. International Journal of Computer Science and Network Security, 8(4), 262–271. Kousalya, K., & Balasubramanie, P. (2009). To Improve Ant Algorithm’s Grid Scheduling Using Local Search. International Journal of Intelligent Information Technology Application, 2(2), 71–79. Koza, J. R., Keane, M. A., Streeter, M. J., Mydlowec, W., Yu, J., & Lanza, G. (2003). Genetic Programming IV Routine Human-Competitive Machine Intelligence. New York: Springer. Krakov, D., & Feitelson, D. G. (2013). High-Resolution Analysis of Parallel Job Workloads. In Proceedings of the 16th International Workshop on Job Scheduling Strategies for Parallel Processing (pp. 178–195). Shanghai. Kromer, P., Platos, J., & Snasel, V. (2012). Independent Task Scheduling by Artificial Immune Systems, Differential Evolution, and Genetic Algorithms. In Proceedings of the 4th International Conference on Intelligent Networking and Collaborative Systems (pp. 28– 32). Bucharest. Kromer, P., Snasel, V. V., Platos, J., Abraham, A., & Izakian, H. (2009). Scheduling Independent Tasks on Heterogeneous Distributed Environments by Differential Evolution. In Proceedings of the International Conference on Intelligent Networking and Collaborative Systems (pp. 170–174). Barcelona. Ku-Mahamud, K. R., & Alobaedy, M. M. (2012). New Heuristic Function in Ant Colony System for Job Scheduling in Grid Computing. In Proceedings of the 17th International Conference on Applied Mathematics (pp. 47–52). Montreux. Ku-Mahamud, K. R., & Nasir, H. J. A. (2010). Ant Colony Algorithm for Job Scheduling in Grid Computing. In Proceedings of the 4th Asia International Conference on Mathematical/ Analytical Modelling and Computer Simulation (pp. 40–45). Kota Kinabalu. doi:10.1109/AMS.2010.21 Kumar, S., Kumar, N., & Kumar, P. (2011). Genetic Algorithm for Network-Aware Job Scheduling in Grid Environment. In Proceedings of the Recent Advances in Intelligent Computational Systems (pp. 615–620). Trivandrum. Li, H., Groep, D., Wolters, L., & Templon, J. (2006). Job Failure Analysis and Its Implications in a Large-scale Production Grid. In Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing (pp. 1–8). Amsterdam. Li, J., & Pan, Y. (2012). A Hybrid Discrete Particle Swarm Optimization Algorithm for Solving Fuzzy Job Shop Scheduling Problem. The International Journal of Advanced Manufacturing Technology, 66(4), 583–596. Li, X., Liao, J., & Cai, M. (2011). Ant Colony Algorithm for Large Scale TSP. In Proceedings of the International Conference on Electrical and Control Engineering (pp. 573–576). Yichang. Lim, C. P., & Jain, L. C. (2009). Advances in Swarm Intelligence. In C. P. Lim, L. C. Jain, & S. Dehuri (Eds.), Innovations in Swarm Intelligence. Heidelberg: Springer. Liu, D., Ma, S., Guo, Z., & Wang, X. (2012). Research of Grid Resource Scheduling Based on Improved Ant Colony Algorithm. In Proceedings of the Third International Conference on Information Computing and Applications (pp. 480– 487). Chengde, China. Liu, H., Abraham, A., & Hassanien, A. E. (2010). Scheduling Jobs on Computational Grids using a Fuzzy Particle Swarm Optimization Algorithm. Journal of Future Generation Computer Systems, 26(8), 1336–1343. doi:10.1016/j.future.2009.05.022 Liu, J., Chen, L., Dun, Y., Liu, L., & Dong, G. (2008). The Research of Ant Colony and Genetic Algorithm in Grid Task Scheduling. In Proceedings of the International Conference on MultiMedia and Information Technology (pp. 47– 49). Three Gorges: Ieee. Liu, K., Chen, J., Jin, H., & Yang, Y. (2009). A Min-Min Average Algorithm for Scheduling Transaction-Intensive Grid Workflows. In Proceedings of the 7th Australasian Symposium on Grid Computing and e-Research (pp. 41–48). Wellington. Liu, L., Song, Y., & Dai, Y. (2010). Cooperative Multi-Ant colony Pseudo-Parallel Optimization Algorithm. In Proceedings of the IEEE International Conference on Information and Automation (pp. 1269–1274). Harbin. doi:10.1109/ICINFA.2010.5512118 Liu, X., Yi, H., & Ni, Z. (2013). Application of Ant Colony Optimization Algorithm in Process Planning Optimization. Journal of Intelligent Manufacturing, 24(1), 1–13. Liusuqin, Shuojun, Menglingfen, & Lixingsheng. (2009). “Making Concessions in Order to Gain Advantages" Improved Ant Colony Optimization for Improving Job Scheduling Problems. In Proceedings of the Global Congress on Intelligent Systems (pp. 115–118). Xiamen. Lope, J. de, Maravall, D., & Quinonez, Y. (2012). Decentralized Multi-tasks Distribution in Heterogeneous Robot Teams by Means of Ant Colony Optimization and Learning Automata. In Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems (pp. 103–114). Salamanca, Spain. Lorpunmanee, S., Sap, M. N., Abdullah, A. H., & Chompoo-inwai, C. (2007). An Ant Colony Optimization for Dynamic Job Scheduling in Grid Environment. International Journal of Computer and Information Science and Engineering, 1(4), 314–321. Lublin, U., & Feitelson, D. G. (2003). The Workload on Parallel Supercomputers: Modeling the Characteristics of Rigid Jobs. Journal of Parallel and Distributed Computing, 63(11), 1105–1122. doi:10.1016/S0743-7315(03)00108-4 Ma, L., Lu, Y., Zhang, F., & Sun, S. (2012). Dynamic Task Scheduling in Cloud Computing Based on Greedy Strategy. In Proceedings of the International Conference on Trustworthy Computing and Services (pp. 156–162). Beijing. Ma, T., Yan, Q., Liu, W., Guan, D., & Lee, S. (2011). Grid Task Scheduling: Algorithm Review. Journal of IETE Technical Review, 28(2), 158–167. MadadyarAdeh, M., & Bagherzadeh, J. (2011). An Improved Ant Algorithm for Grid Scheduling Problem Using Biased Initial Ants. In Proceedings of the 3rd International Conference on Computer Research and Development (pp. 373– 378). Shanghai. Magoules, F., Nguyen, T.-M.-H., & Yu, L. (2009). Grid Resource Management : Toward Virtual and Services Compliant Grid Computing. Boca Raton: CRC Press. Magoules, F., Pan, I., Tan, K.-A., & Kumar, A. (2009). Introduction to Grid Computing. Boca Raton: CRC Press. Maheshbhai, L. A. (2011). Job Scheduling Based on Reliability, Time and Cost Constraints under Grid Environment. In Proceedings of the Nirma University International Conference on Engineering (pp. 1–5). Ahmedabad. Maheswaran, M., Ali, S., Siegel, H. J., Hensgen, D., & Freund, R. F. (1999). Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems. In Proceedings of the 8th Heterogeneous Computing Workshop (pp. 30–44). San Juan. Malarvizhi, N., & Uthariaraj, V. R. (2009). A Minimum Time to Release Job Scheduling Algorithm in Computational Grid Environment. In Proceedings of the 5th International Joint Conference on INC, IMS and IDC (pp. 13–18). Seoul. Mao, J. (2011). A Task Scheduling Method of Grid Service using Ant Colony Optimization. In Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (pp. 2752–2755). Zhengzhou, China. Maruthanayagam, D., & UmaRani, R. (2010). Enhanced Ant Colony Algorithm for Grid Scheduling. International Journal of Computer Technology and Applications, 1(1), 43–53. Mathiyalagan, P., Suriya, S., & Sivanandam, S. N. (2010). Modified Ant Colony Algorithm for Grid Scheduling. International Journal on Computer Science and Engineering, 02(02), 132–139. Meihong, W., Wenhua, Z., Wang, M., & Zeng, W. (2010). A Comparison of Four Popular Heuristics for Task Scheduling Problem in Computational Grid. In Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing (pp. 1–4). Chengdu. doi:10.1109/WICOM.2010.5600872 Menasce, D. A., Saha, D., Porto, S. C. D. S., Almeida, V. A. F., & Tripathi, S. K. (1995). Static and Dynamic Processor Scheduling Disciplines in Heterogeneous Parallel Architectures. Journal of Parallel and Distributed Computing, 28(1), 1–18. doi:10.1006/jpdc.1995.1085 Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Heidelberg: Springer-Verlag. Michalewicz, Z. (1999). Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer-Verlag. Michelakos, I., Mallios, N., Papageorgiou, E., & Vassilakopoulos, M. (2011). Ant Colony Optimization and Data Mining. In N. Bessis & F. Xhafa (Eds.), Next Generation Data Technologies for Collective Computational Intelligence (pp. 31–60). Heidelberg: Springer. doi:10.1007/978-3-642-20344-2_2 Montes, J., Sanchez, A., & Perez, M. S. (2012). Riding Out the Storm: How to Deal with the Complexity of Grid and Cloud Management. Journal of Grid Computing, 10(3), 349–366. doi:10.1007/s10723-012-9225-4 Moscato, P., & Cotta, C. (2010). A Modern Introduction to Memetic Algorithms. In M. Gendreau & J.-Y. Potvin (Eds.), Handbook of Metaheuristics (pp. 141–183). New York: Springer. Mou, L.-M. (2011). A Novel Ant Colony System with Double Pheromones for the Generalized TSP. In Proceedings of the 7th International Conference on Natural Computation (pp. 1923–1928). Shanghai. Nagariya, S., Mishra, M., & Shrivastava, M. (2014). An Inherent Approach based on ACO and Tabu Search for Resource Allocation in Grid Environment. International Journal of Computer Applications, 87(6), 39–45. Nayak, S. K., Padhy, S. K., Panigrahi, S. P., Kumari, S., & Prasada, S. (2012). A Novel Algorithm for Dynamic Task Scheduling. Journal of Future Generation Computer Systems, 28(5), 709–717. doi:10.1016/j.future.2011.12.001 Nithya, L. M., Nadu, T., & Shanmugam, A. (2011). Scheduling in Computational Grid with a New Hybrid Ant Colony Optimization Algorithm. European Journal of Scientific Research, 62(2), 273–281. Noghanian, S., Sabouni, A., Desell, T., & Ashtari, A. (2014). Global Optimization Differential Evolution, Genetic Algorithms, Particle Swarm, and Hybrid Methods. In S. Noghanian, A. Sabouni, T. Desell, & A. Ashtari (Eds.), Microwave Tomography Global Optimization, Parallelization and Performance Evaluation (pp. 39–61). New York: Springer. Nothegger, C., Mayer, A., Chwatal, A., & Raidl, G. R. (2012). Solving the Post Enrolment Course Timetabling Problem by Ant Colony Optimization. Journal of Annals of Operations Research, 194 (1), 325–339. doi:10.1007/s10479-012-1078-5 Pace, D. K. (2003). Verification, Validation, and Accreditation of Simulation Models. In M. S. Obaidat & G. I. Papadimitriou (Eds.), Applied System Simulation Methodologies and Applications. New York: Springer. Passos, W. Dos. (2009). Numerical Methods, Algorithms and Tools in C#. Boca Raton: CRC Press. Pereira, C., Goncalves, L., & Ferreira, M. (2013). Optic Disc Detection in Color Fundus Images Using Ant Colony Optimization. Journal of Medical & Biological Engineering & Computing, 51(3), 295–303. Phatanapherom, S., Uthayopas, P., & Kachitvichyanukul, V. (2003). Fast Simulation Model for Grid Scheduling Using Hypersim. In Proceedings of the Winter Simulation Conference (pp. 1494 – 1500). New Orleans, LA. Pintea, C.-M. (2014). Advances in Bio-inspired Computing for Combinatorial Optimization Problems. Berlin Heidelberg: Springer. Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle Swarm Optimization. Journal of Swarm Intelligence, 1(a), 33–57. doi:10.1007/s11721-007-0002-0 Pooranian, Z., Shojafar, M., Abawajy, J. H., & Abraham, A. (2013). An efficient meta-heuristic algorithm for grid computing. Journal of Combinatorial Optimization, 1–22. doi:10.1007/s10878-013-9644-6 Price, K. V., Storn, R. M., & Lampinen, J. A. (2005). Differential Evolution A Practical Approach to Global Optimization. Berlin: Springer. Qureshi, M. B., Dehnavi, M. M., Min-Allah, N., Qureshi, M. S., Hussain, H., Rentifis, I., … Zomaya, A. Y. (2014). Survey on Grid Resource Allocation Mechanisms. Journal of Grid Computing, 12(2), 399–441. doi:10.1007/s10723-014-9292-9 Rajni, & Chana, I. (2013). Bacterial Foraging Based Hyper-Heuristic for Resource Scheduling in Grid Computing. Journal of Future Generation Computer Systems, 29(3), 751–762. doi:10.1016/j.future.2012.09.005 Raju, R., Babukarthik, R. G., & Dhavachelvan, P. (2013). Hybrid Ant Colony Optimization and Cuckoo Search Algorithm for Job Scheduling. In Proceedings of the 2nd International Conference on Advances in Computing and Information Technology (pp. 491–501). Chennai. Reeves, C. R., & Rowe, J. E. (2003). Genetic Algorithms: Principles and Perspectives A Guide to GA Theory. Boston: Kluwer Academic Publishers. Ritchie, G., & Levine, J. (2003). A Fast, Effective Local Search for Scheduling Independent Jobs in Heterogeneous Computing Environments. In Proceedings of the 22nd Workshop of the UK Planning and Scheduling Special Interest Group (pp. 178–183). Glasgow. Ritchie, G., & Levine, J. (2004). A Hybrid Ant Algorithm for Scheduling Independent Jobs in Heterogeneous Computing Environments. In Proceedings of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group (pp. 1–7). Cork. Rothlauf, F. (2011). Design of Modern Heuristics Principles and Application. Heidelberg: Springer. Salman, A., Ahmad, I., & Al-Madani, S. (2002). Particle Swarm Optimization for Task Assignment Problem. Journal of Microprocessors and Microsystems, 26(8), 363–371. Schwefel, H.-P. (1995). Evolution and Optimum Seeking. New York: Wiley. See, P. C., Tai, V. C., & Molinas, M. (2012). Ant Colony Optimization Applied to Control of Ocean Wave Energy Converters. Journal of Energy Procedia, 20(1), 148–155. Selvi, S., & Manimegalai, D. (2010). Scheduling Jobs on Computational Grid using Differential Evolution Algorithm. In Proceedings of the 12th International Conference on Networking, VLSI and Signal Processing (pp. 118–123). University of Cambridge, UK. Seo, K.-K. (2012). An Ant Colony Optimization Algorithm Based Image Classification Method for Content-Based Image Retrieval in Cloud Computing Environment. In Proceedings of the International Conferences on Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition (pp. 110–117). Jeju Island. Shang, J., Zhang, J., Lei, X., Zhang, Y., & Chen, B. (2012). Incorporating Heuristic Information into Ant Colony Optimization for Epistasis Detection. Journal of Genes & Genomics, 34(3), 321–327. Silva, D. P. da, Cirne, W., & Brasileiro, F. V. (2003). Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids. In Proceedings of the 9th International Euro-Par Conference on Parallel Processing (pp. 169– 180). Klagenfurt. doi:10.1007/978-3-540-45209-6_26 Sim, K. M. (2009). Special Issue on Grid Resource Management. IEEE SYSTEMS JOURNAL, 3(1), 2–5. Singh, L., & Singh, S. (2014). A Genetic Algorithm for Scheduling Workflow Applications in Unreliable Cloud Environment. In Proceedings of the 2nd International Conference on Recent Trends in Computer Networks and Distributed Systems Security (pp. 139–150). Trivandrum. Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to Genetic Algorithms. Electronics (Vol. 2, p. 442). Heidelberg: Springer. doi:10.1007/978-3-540-73190-0 Smith, A. J. (2007). Workloads. Journal of Communications of the ACM, 50(11), 45–50. Song, X., Sun, L., & Cao, Y. (2010). Study on the Convergence of Converse Ant Colony Algorithm for Job Shop Scheduling Problem. In Proceedings of the 6th International Conference on Natural Computation (pp. 2710–2714). Yantai. Sonmez, O., Yigitbasi, N., Abrishami, S., Iosup, A., & Epema, D. (2010). Performance Analysis of DynamicWorkflow Scheduling in Multicluster Grids. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (pp. 49–60). Chicago. Stoean, C., & Stoean, R. (2014). Support Vector Machines and Evolutionary Algorithms for Classification. Switzerland: Springer. Stutzle, T., & Hoos, H. (1997). MAX-MIN Ant System and Local Search for the Traveling Salesman Problem. In Proceedings of the International Conference on Evolutionary Computation (pp. 309–314). Indianapolis. doi:10.1109/ICEC.1997.592327 Stutzle, T., & Hoos, H. H. (2000). MAX-MIN Ant System. Journal of Future Generation Computer Systems, 16(8), 889–914. doi:10.1016/S0167- 739X(00)00043-1 Talbi, E. (2013a). A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning. In E. Talbi (Ed.), Hybrid Metaheuristics. Heidelberg: Springer. Talbi, E. (2013b). Hybrid Metaheuristics. Heidelberg: Springer. Thesen, A. (1998). Design and Evaluation of Tabu Search Algorithms for Multiprocessor Scheduling. Journal of Heuristics, 4(2), 141–160. Tian, J., Yu, W., Chen, L., & Ma, L. (2011). Image Edge Detection Using Variation-Adaptive Ant Colony Optimization. In N. T. Nguyen (Ed.), Transactions on Computational Collective Intelligence V (pp. 27–40). New York: Springer. Tian, Y., Liu, D., Yuan, D., & Wang, K. (2012). A Discrete PSO for Two-Stage Assembly Scheduling Problem. The International Journal of Advanced Manufacturing Technology, 66(4), 481–499. Tiwari, P., & Verma, B. (2012). Application of Ant Colony Algorithm for Classification and Rule Generation of Data. In S. Patnaik & Y.-M. Yang (Eds.), Soft Computing Techniques in Vision Science (pp. 155–170). Berlin: Springer. doi:10.1007/978-3-642-25507-6_14 Tsutsui, S., & Fujimoto, N. (2013). ACO withTabu Searchon GPUs for Fast Solution of the QAP. In S. Tsutsui & P. Collet (Eds.), Massively Parallel Evolutionary Computation on GPGPUs (pp. 179–202). Heidelberg: Springer. Ullman, J. D. (1975). NP-Complete Scheduling Problems. Journal of Computer and System Sciences, 10(3), 384–393. Url, S., Archive, T. J., Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Journal of Science, 220(4598), 671–680. Vidal, R. V. V. (1993). Applied Simulated Annealing. Berlin Heidelberg: Springer. Visalakshi, P., & Sivanandam, S. N. (2009). Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm Optimization. International Journal of Open Problems Compt Math, 2(3), 475–488. Vob, S. (2001). Meta-Heuristics: The State of the Art. In Proceedings of the Workshop on Local Search for Planning and Scheduling (pp. 1–23). Heidelberg. Wang, J., Duan, Q., Jiang, Y., & Zhu, X. (2010). A New Algorithm for Grid Independent Task Schedule ： Genetic Simulated Annealing. In Proceedings of the World Automation Congress (pp. 165–171). Kobe. Wang, Y., Zhang, J., Zhao, Y., Wang, J., & Gu, W. (2013). ACO-Based Routing and Spectrum Allocation in Flexible Bandwidth Networks. Journal of Photonic Network Communications, 25(3), 135–143. doi:10.1007/s11107-013-0397-z Wang, Z., Jing, X., & Wang, J. (2012). A Novel Routing Algorithm Based on Ant Colony Optimization for Hierarchical Wireless Sensor Networks. In Proceedings of the International Conference on Electrics, Communication and Automatic Control (pp. 825–831). Chongqing, China. Wankar, R. (2008). Grid Computing with Globus: an Overview and Research Challenges. International Journal of Computer Science and Applications, 5(3), 56–69. Wei, L., Zhang, X., Li, Y., & Li, Y. (2012). An Improved Ant Algorithm for Grid Task Scheduling Strategy. Journal of Physics Procedia, 24(1), 1974–1981. doi:10.1016/j.phpro.2012.02.290 Wiener, R., & Of, O. (2009). Ant Colony System Optimization. Journal of Object Technology, 8(6), 39–58. Wolski, R., Spring, N. T., & Hayes, J. (1999). The Network Weather Service: a Distributed Resource Performance Forecasting Service for Metacomputing. Journal of Future Generation Computer Systems, 15(5-6), 757–768. doi:10.1016/S0167-739X(99)00025-4 Wu, C., Zhang, N., Jiang, J., Yang, J., & Liang, Y. (2007). Improved Bacterial Foraging Algorithms and Their Applications to Job Shop Scheduling Problems. In Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (pp. 562–569). Warsaw. Xhafa, F. (2006). An Experimental Study on GA Replacement Operators for Scheduling on Grids. In Proceedings of the 2nd International Conference on Bioinspired Optimization Methods and their Applications (pp. 121–130). Ljubljana. Xhafa, F. (2007). A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids. In A. Abraham, C. Grosan, & H. Ishibuchi (Eds.), Hybrid Evolutionary Algorithms (pp. 269–311). Heidelberg: Springer. Xhafa, F., & Abraham, A. (2008a). Meta-heuristics for Grid Scheduling Problems. In F. Xhafa & A. Abraham (Eds.), Metaheuristics for Scheduling in Distributed Computing Environments (pp. 1–37). Heidelberg: Springer. doi:10.1007/978-3-540-69277-5_1 Xhafa, F., & Abraham, A. (2008b). Metaheuristics for Scheduling in Distributed Computing Environments. Berlin: Springer. Xhafa, F., & Abraham, A. (2009). A Compendium of Heuristic Methods for Scheduling in Computational Grids. In Proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning (pp. 751– 758). Burgos. doi:10.1007/978-3-642-04394-9_92 Xhafa, F., & Abraham, A. (2010). Computational Models and Heuristic Methods for Grid Scheduling Problems. Journal of Future Generation Computer Systems, 26(4), 608–621. doi:doi:10.1016/j.future.2009.11.005 Xhafa, F., Alba, E., & Dorronsoro, B. (2007). Efficient Batch Job Scheduling in Grids using Cellular Memetic Algorithms. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (pp. 1 – 8). Long Beach, CA. Xhafa, F., Alba, E., Dorronsoro, B., Duran, B., & Abraham, A. (2008). Efficient Batch Job Scheduling in Grids Using Cellular Memetic Algorithms. Journal of Mathematical Modelling and Algorithms, 7(2), 217–236. Xhafa, F., Barolli, L., & Durresi, A. (2007a). An Experimental Study on Genetic Algorithms for Resource Allocation on Grid Systems. Journal of Interconnection Networks, 8(4), 427–443. Xhafa, F., Barolli, L., & Durresi, A. (2007b). Batch Mode Scheduling in Grid Systems. International Journal of Web and Grid Services, 3(1), 19–37. Xhafa, F., Barolli, L., & Durresi, A. (2007c). Immediate Mode Scheduling of Independent Jobs in Computational Grids. In Proceedings of the 21st International Conference on Advanced Networking and Applications (pp. 970–977). Niagara Falls. Xhafa, F., & Carretero, J. (2009). Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments. In E. Alba, C. Blum, P. Isasi, C. Leon, & J. A. Gomez (Eds.), Optimization Techniques for Solving Complex Problems (pp. 423–441). Hoboken, N.J: Wiley. Xhafa, F., Carretero, J., Alba, E., & Dorronsoro, B. (2008). Design and Evaluation of Tabu Search Method for Job Scheduling in Distributed Environments. In Proceedings of the International Symposium on Parallel and Distributed Processing (pp. 1 – 8). Miami, FL. Xhafa, F., Carretero, J., Barolli, L., & Durresi, A. (2007). Requirements for an Event- Based Simulation Package for Grid Systems. Journal of Interconnection Networks, 08(02), 163–178. doi:10.1142/S0219265907001965 Xhafa, F., Carretero, J., Dorronsoro, B. B., & Alba, E. (2009). A Tabu Search Algorithm for Scheduling Independent Jobs in Computational Grids. Journal of Computing and Informatics, 28(2), 237–250. Xhafa, F., & Duran, B. (2008). Parallel Memetic Algorithms for Independent Job Scheduling in Computational Grids. In C. Cotta & J. van Hemert (Eds.), Recent Advances in Evolutionary Computation for Combinatorial Optimization (pp. 219–239). Heidelberg: Springer. doi:10.1007/978-3-540-70807-0_14 Xhafa, F., Duran, B., Abraham, A., & Dahal, K. P. (2008). Tuning Struggle Strategy in Genetic Algorithms for Scheduling in Computational Grids. In Proceedings of the 7th Computer Information Systems and Industrial Management Applications (pp. 275–280). Ostrava. Xhafa, F., Duran, B., & Kolodziej, J. (2011). On Exploitation vs Exploration of Solution Space for Grid Scheduling. In Proceedings of the 3rd International Conference on Intelligent Networking and Collaborative Systems (pp. 164–171). Fukuoka. doi:10.1109/INCoS.2011.128 Xhafa, F., Gonzalez, J. A., Dahal, K. P., & Abraham, A. (2009). A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids. In Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems (pp. 285–292). Salamanca. doi:10.1007/978-3-642-02319-4_34 Xhafa, F., Kolodziej, J., Barolli, L., & Fundo, A. (2011). A GA+TS Hybrid Algorithm for Independent Batch Scheduling in Computational Grids. In Proceedings of the 14th International Conference on Network-Based Information Systems (pp. 229–235). Tirana. doi:10.1109/NBiS.2011.41 Xhafa, F., Kolodziej, J., Barolli, L., Kolici, V., Miho, R., & Takizawa, M. (2011).Evaluation of Hybridization of GA and TS Algorithms for Independent Batch Scheduling in Computational Grids. In Proceedings of the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 148–155). Barcelona. doi:10.1109/3PGCIC.2011.31 Xhafa, F., Koodziej, J., Duran, B., Bogdanski, M., & Barolli, L. (2011). A Comparison Study on the Performance of Population-based Meta-Heuristics for Independent Batch Scheduling in Grid Systems. In Proceedings of the International Conference on Complex Intelligent and Software Intensive Systems (pp. 123–130). Seoul. Xing, B., & Gao, W.-J. (2014). Bacteria Inspired Algorithms. In B. Xing & W.-J. Gao (Eds.), Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms (pp. 21–38). Cham: Springer. Yan, H. U. I., Shen, X., Li, X., & Wu, M. (2005). An Improved Ant Algorithm for Job Scheduling in Grid Computing. In Proceedings of the 4th International Conference on Machine Learning and Cybernetics (pp. 2957– 2961). Guangzhou. doi:10.1109/ICMLC.2005.1527448 Yang, X.-S. (2014). Nature-Inspired Optimization Algorithms. Amsterdam: Elsevier. YarKhan, A., & Dongarra, J. J. (2002). Experiments with Scheduling Using Simulated Annealing in a Grid Environment. In Proceedings of the 3rd International Workshop on Grid Computing (pp. 232–242). Baltimore. Yin, J., & Xiang, W. (2012). Ant Colony Algorithm for Surgery Scheduling Problem. In Proceedings of the Third International Conference on Advances in Swarm Intelligence (pp. 198–205). Shenzhen, China. Youhui, L., Xinhua, L., & Qi, L. (2012). Assembly Sequence Planning Based on Ant Colony Algorithm. In Y. Zhang (Ed.), Future Communication, Computing, Control and Management (Vol. 141, pp. 397–404). Heidelberg: Springer. Yu, H., Ni, J., & Zhao, J. (2013). ACO Sampling: An Ant Colony Optimization-Based Under sampling Method for Classifying Imbalanced DNA Microarray Data. Journal of Neurocomputing, 101(1), 309–318. doi:10.1016/j.neucom.2012.08.018 Yu, J., & Wang, C. (2013). A Max–Min Ant Colony System for Assembly Sequence Planning. International Journal of Advanced Manufacturing Technology, 67(9), 2819–2835. Yu, X., & Gen, M. (2010). Introduction to Evolutionary Algorithms. London: Springer. Zapfel, G., Braune, R., & Bogl, M. (2010). Metaheuristic Search Concepts a Tutorial with Applications to Production and Logistics. Heidelberg: Springer. Zhang, S., Ning, T., & Zhang, Z. (2012). A New Hybrid Ant Colony Algorithm for Solving Vehicle Scheduling Problem. International Journal of Advancements in Computing Technology, 4(5), 17–23. Zhang, T., Lin, J., Qiu, B., & Fu, Y. (2011). Solving the Aircraft Assigning Problem by the Ant Colony Algorithm. In Proceedings of the International Conference on Information and Management Engineering (pp. 179–187). Wuhan, China. Zheng, Q., Li, M., Li, Y., & Tang, Q. (2013). Station Ant Colony Optimization for the Type 2 Assembly Line Balancing Problem. The International Journal of Advanced Manufacturing Technology, 66(9), 1859–1870. Zhong, L., Long, Z., Zhang, J., & Song, H. (2011). An Efficient Memetic Algorithm for Job Scheduling in Computing Grid. In Proceedings of the International Symposium on Information and Automation (pp. 650–656). Guangzhou. Zhu, P., Zhao, M., & He, T. (2010a). A Novel Ant Colony Algorithm for Grid Task Scheduling. Journal of Computational Information Systems, 6(3), 745–752. Zhu, P., Zhao, M., & He, T. (2010b). A Novel Ant Colony Optimization Algorithm in Application of Pheromone Diffusion. In Proceedings of the International Conference on Life System Modeling and Simulation (pp. 1–8). Wuxi, China. Zhu, Y., & Wei, Q. (2010). An Improved Ant Colony Algorithm for Independent Tasks Scheduling of Grid. In Proceedings of the 2nd International Conference on Computer and Automation Engineering (pp. 566–569). Singapore.

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Similar Items