skip to main content
survey

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Published:21 July 2015Publication History
Skip Abstract Section

Abstract

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.

References

  1. L. Agostinho, G. Feliciano, L. Olivi, E. Cardozo, and E. Guimaraes. 2011. A bio-inspired approach to provisioning of virtual resources in federated clouds. In Proceedings of the IEEE 9th International Conference on Dependable, Autonomic and Secure Computing. 598--604. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Y. Ajiro and A. Tanaka. 2007. Improving packing algorithms for server consolidation. In Proceedings of the International Conference for the Computer Measurement Group, 399--406.Google ScholarGoogle Scholar
  3. E. Apostol, I. Baluta, A. Gorgoi, and V. Cristea. 2011. Efficient manager for virtualized resource provisioning in cloud systems. In Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing. 511--517.Google ScholarGoogle Scholar
  4. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, et al. 2010. A view of cloud computing. Communications of the ACM 53, 4 (2010), 50--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. G. Babukarthik, R. Raju, and P. Dhavachelvan. 2012. Energy-aware scheduling using hybrid algorithm for cloud computing. In Proceedings of the 3rd International Conference on Computing Communication & Networking Technologies. 1--6.Google ScholarGoogle Scholar
  6. T. Back, M. Emmerich, and O. M. Shir. 2008. Evolutionary algorithms for real world applications. IEEE Computational Intelligence Magazine 3, 1 (2008), 64--67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Baliga, R. W. A. Ayre, K. Hinton, and R. S. Tucker. 2011. Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE 99, 1 (2011), 149--167.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Banerjee, I. Mukherjee, and P. K. Mahanti. 2009. Cloud computing initiative using modified ant colony framework. World Academy of Science, Engineering and Technology 56 (2009), 221--224.Google ScholarGoogle Scholar
  9. A. K. Bardsiri and S. M. Hashemi. 2012. A review of workflow scheduling in cloud computing environment. International Journal of Computer Science and Management Research 1, 3 (2012), 348--351.Google ScholarGoogle Scholar
  10. E. Barrett, E. Howley, and J. Duggan. 2011. A learning architecture for scheduling workflow applications in the cloud. In Proceedings of the 9th IEEE European Conference on Web Services. 83--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandicc. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25 (2009), 599--616. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Chaisiri, B. Lee, and D. Niyato. 2012. Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing 5, 2 (2012), 164--177. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Y. Chawla and M. Bhonsle. 2012. A study on scheduling methods in cloud computing. International Journal of Emerging Trends & Technology in Computer Science 1, 3 (2012), 12--17.Google ScholarGoogle Scholar
  14. N. Chen, W. N. Chen, Y. J. Gong, Z. H. Zhan, J. Zhang, Y. Li, and Y. S. Tan. 2014. An evolutionary algorithm with double-level archives for multiobjective optimization. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2014.2360923Google ScholarGoogle Scholar
  15. S. Chen, J. Wu, and Z. H. Lu. 2012. A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness. In Proceedings of the IEEE 12th International Conference on Computer and Information Technology. 177--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. W. N. Chen and J. Zhang. 2012. A set-based discrete PSO for cloud workflow scheduling with user-defined QoS constraints. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. 773--778.Google ScholarGoogle Scholar
  17. Z. G. Chen, K. J. Du, Z. H. Zhan, and J. Zhang. 2015. Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In Proceedings of the IEEE Congress on Evolutionary Computation, in press.Google ScholarGoogle Scholar
  18. L. Chimakurthi and M. Kumar. 2011. Power efficient resource allocation for clouds using ant colony framework. Arxiv preprint arXiv:1102.2608, 1--6.Google ScholarGoogle Scholar
  19. H. Choi, S. H. Lee, and D. I. Park. 2013. Biologic data analysis platform based on the cloud. International Journal of Bio-Science and Bio-Technology, 5, 3 (2013), 199--206.Google ScholarGoogle Scholar
  20. G. Copil, D. Moldovan, I. Salomie, T. Cioara, I. Anghel, and D. Borza. 2012. Cloud SLA negotiation for energy saving—A particle swarm optimization approach. In Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing. 289--296.Google ScholarGoogle Scholar
  21. M. J. Csorba, H. Meling, and P. E. Heegaard. 2010. Ant system for service deployment in private and public clouds. In Proceedings of the 2nd Workshop on Bio-Inspired Algorithms for Distributed Systems. 19--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. Dasgupta, B. Mandal, P. Dutta, J. K. Mondal, and S. Dam. 2013. A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology 10 (2013), 340--347.Google ScholarGoogle ScholarCross RefCross Ref
  23. S. Di, C. L. Wang, and F. Cappello. 2014. Adaptive algorithm for minimizing cloud task length with prediction errors. IEEE Transactions on Cloud Computing 2, 2 (2014), 194--207.Google ScholarGoogle ScholarCross RefCross Ref
  24. M. D. Dikaiakos, G. Pallis, D. Katsaros, P. Mehra, and A. Vakali. 2009. Cloud computing: Distributed Internet computing for IT and scientific research. IEEE Internet Computing 13, 5 (2009), 10--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. A. Dragland. 2013. Big data, for better or worse: 90% of world's data generated over last two years. Science Daily (May 2013).Google ScholarGoogle Scholar
  26. B. El Zant, I. Amigo, and M. Gagnaire. 2014. Federation and revenue sharing in cloud computing environment. In Proceedings of the IEEE International Conference on Cloud Engineering. 446--451. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. Feller and C. Morin. 2012. Autonomous and energy-aware management of large-scale cloud infrastructures. In Proceedings of the IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum. 2542--2545. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. E. Feller, L. Rilling, and C. Morin. 2011. Energy-aware ant colony based workload placement in clouds. In Proceedings of the 12th IEEE/ACM International Conference on Grid Computing. 26--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. I. Foster, Y. Zhao, I. Raicu, and S. Lu. 2008. Cloud computing and grid computing 360-degree compared. In Proceedings of the Grid Computing Environments Workshop, 1--10.Google ScholarGoogle Scholar
  30. G. Galante and L. C. E. D. Bona. 2012. A survey on cloud computing elasticity. In Proceedings of the IEEE/ACM 5th International Conference on Utility and Cloud Computing. 263--270. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. G. N. Gan, T. L. Huang, and S. Gao. 2010. Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In Proceedings of the International Conference on Intelligent Computing and Integrated Systems. 60--63.Google ScholarGoogle Scholar
  32. Y. Q. Gao, H. B. Guan, Z. W. Qi, Y. Hou, L. Liu. 2013. A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of Computer and System Sciences 79 (2013), 1230--1242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. J. W. Ge and Y. S. Yuan. 2013. Research of cloud computing task scheduling algorithm based on improved genetic algorithm. In Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. 2134--2137.Google ScholarGoogle Scholar
  34. T. A. L. Genez, L. F. Bittencourt, and E. R. M. Madeira. 2012. Workflow scheduling for SaaS/PaaS cloud providers considering two SLA levels. In Proceedings of the IEEE Network Operations and Management Symposium. 906--912.Google ScholarGoogle ScholarCross RefCross Ref
  35. O. Givehchi, H. Trsek, and J. Jasperneite. 2014. Cloud computing for industrial automation systems—A comprehensive overview. In Proceedings of the 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation. 1--4.Google ScholarGoogle Scholar
  36. T. Grandison, E. M. Maximilien, S. Thorpe, and A. Alba. 2010. Towards a formal definition of a computing cloud. In Proceedings of the IEEE 6th World Congress on Services. 191--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. L. Z. Guo, S. G. P. Zhao, S. G. Shen, and C. Y. Jiang. 2012. A particle swarm optimization for data placement strategy in cloud computing. Information Engineering and Applications, Lecture Notes in Electrical Engineering, 323--330.Google ScholarGoogle Scholar
  38. W. Guo and X. Wang. 2013. A data placement strategy based on genetic algorithm in cloud computing platform. In Proceedings of the 10th Web Information System and Application. 369--372. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. L. G. He, D. Q. Zou, Z. Zhang, H. Jin, K. Yang, and S. A. Jarvis. 2011. Optimizing resource consumptions in clouds. In Proceedings of the 12th IEEE/ACM International Conference on Grid Computing. 42--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. L. Heilig and S. Vob. 2014. A scientometric analysis of cloud computing literature. IEEE Transactions on Cloud Computing 2, 3 (2014), 266--278.Google ScholarGoogle ScholarCross RefCross Ref
  41. J. H. Hu, J. H. Gu, G. F. Sun, and T. H. Zhao. 2010. A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In Proceedings of the 3rd International Symposium on Parallel Architectures, Algorithms and Programming. 89--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. H. Jang, T. Y. Kim, J. K. Kim, and J. S. Lee. 2012. The study of genetic algorithm-based task scheduling for cloud computing. International Journal of Control and Automation 5, 4 (2012), 157--162.Google ScholarGoogle Scholar
  43. B. Jennings and R. Stadler. 2014. Resource management in clouds: Survey and research challenges. Journal of Network Systems Management 1--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. K. Jindarak and P. Uthayopas. 2011. Performance improvement of cloud storage using a genetic algorithm based placement. In Proceedings of the 8th International Joint Conference on Computer Science and Software Engineering. 54--57.Google ScholarGoogle Scholar
  45. H. Kaur and M. Singh. 2012. Review of various scheduling techniques in cloud computing. International Journal of Networking & Parallel Computing 1, 2 (2012).Google ScholarGoogle Scholar
  46. Y. Kessaci, N. Melab, and E. G. Talbi. 2011. A Pareto-based GA for scheduling HPC applications on distributed cloud infrastructures. In Proceeding of the International Conference on High Performance Computing and Simulation. 456--462.Google ScholarGoogle Scholar
  47. T. S. Kuhn. 2012. The Structure of Scientific Revolutions, University of Chicago Press.Google ScholarGoogle Scholar
  48. P. Kumar and A. Verma. 2012. Independent task scheduling in cloud computing by improved genetic algorithm. International Journal of Advanced Research in Computer Science and Software Engineering 2, 5 (2012), 111--114.Google ScholarGoogle Scholar
  49. S. Kumar and P. Balasubramanie. 2012. Dynamic scheduling for cloud reliability using transportation problem. Journal of Computer Science 8, 10 (2012), 1615--1626.Google ScholarGoogle ScholarCross RefCross Ref
  50. F. Larumbe and B. Sanso. 2013. A tabu search algorithm for the location of data centers and software components in green cloud computing networks. IEEE Transactions on Cloud Computing 1, 1 (2013), 22--35.Google ScholarGoogle ScholarCross RefCross Ref
  51. G. Lee, N. Tolia, P. Ranganathan, and R. H. Katz. 2011. Topology-aware resource allocation for data-intensive workloads. ACM SIGCOMM Computer Communication Review 41 (2011), 120--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. J. Lee, H.-A. Kao, and S. Yang. 2014. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16 (2014), 3--8.Google ScholarGoogle ScholarCross RefCross Ref
  53. H. H. Li, Y. W. Fu, Z. H. Zhan, and J. J. Li. 2015a. Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling. In Proceedings of the IEEE Congress on Evolution Computation, in press.Google ScholarGoogle Scholar
  54. H. H. Li, Z. G. Chen, Z. H. Zhan, K. J. Du, and J. Zhang. 2015b. Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment. In Proceedings of the Genetic Evolutionary Computation Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. K. Li, G. C. Xu, G. Y. Zhao, Y. S. Dong, and D. Wang. 2011. Cloud task scheduling based on load balancing ant colony optimization. In Proceedings of the 6th Annual ChinaGrid Conference. 3--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Q. Li and Y. K. Guo. 2010. Optimization of resource scheduling in cloud computing. In Proceedings of the 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. 315--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. X. D. Li and X. Yao. 2012. Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation 16, 2 (2012), 210--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Y. H. Li, Z. H. Zhan, S. J. Lin, J. Zhang, and X. N. Luo. 2015c. Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Information Sciences 293 (2015), 370--382.Google ScholarGoogle ScholarCross RefCross Ref
  59. Y. L. Li, Z. H. Zhan, Y. J. Gong, W. N. Chen, J. Zhang, and Y. Li. 2014. Differential evolution with an evolution path: A DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2014.2360752Google ScholarGoogle Scholar
  60. Y. L. Li, Z. H. Zhan, Y. J. Gong, J. Zhang, Y. Li, and Q. Li. 2015d. Fast micro-differential evolution for topological active net optimization, IEEE Transactions on Cybernetics, in press.Google ScholarGoogle Scholar
  61. Y. C. Lin, C. S. Yu, and Y. J. Lin. 2013. Enabling large-scale biomedical analysis in the cloud. BioMed Research International, 2013, Article ID 185679, 1--6.Google ScholarGoogle Scholar
  62. H. Liu, D. Xu, and H. K. Miao. 2011. Ant colony optimization based service flow scheduling with various QoS requirements in cloud computing. In Proceedings of the 1st ACIS International Symposium on Software and Network Engineering. 53--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. J. Liu and T. L. Huang. 2010. Dynamic route scheduling for optimization of cloud database. In Proceedings of the International Conference on Intelligent Computing and Integrated Systems. 680--682.Google ScholarGoogle Scholar
  64. X. F. Liu, Z. H. Zhan, K. J. Du, and W. N. Chen. 2014. Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In Proceedings of the Genetic and Evolutionary Computation Conference, 41--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. X. Lu and Z. L. Gu. 2011. A load-adaptive cloud resource scheduling model based on ant colony algorithm. In Proceedings of the IEEE International Conference on Cloud Computing and Intelligence Systems. 296--300.Google ScholarGoogle Scholar
  66. Q. C. Lv, X. X. Shi, and L. Z. Zhou. 2012. Based on ant colony algorithm for cloud management platform resources scheduling. In Proceedings of the World Automation Congress. 1--4.Google ScholarGoogle Scholar
  67. C. C. T. Mark, D. Niyato, and C. K. Tham. 2011. Evolutionary optimal virtual machine placement demand forecaster for cloud computing. In Proceedings of the International Conference on Advanced Information Networking and Applications. 348--355. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. H. B. Mi, H. M. Wang, G. Yin, Y. F. Zhou, D. X. Shi, and L. Yuan. 2010. Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In Proceedings of the IEEE International Conference on Services Computing. 514--521. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. H. Morshedlou and M. R. Meybodi. 2014. Decreasing impact of SLA violations: A proactive resource allocation approach for cloud computing environments. IEEE Transactions on Cloud Computing 2, 2 (2014), 156--167.Google ScholarGoogle ScholarCross RefCross Ref
  70. A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C. A. Coello Coello. 2014. A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 4--19.Google ScholarGoogle ScholarCross RefCross Ref
  71. H. Nakada, T. Hirofuchi, H. Ogawa, and S. Itoh. 2009. Toward virtual machine packing optimization based on genetic algorithm. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, Lecture Notes in Computer Science, Volume 5518, 651--65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. P. Singh, N. Nitin, and R. Rastogi. 2012. Load balancing of nodes in cloud using ant colony optimization. In Proceedings of the 14th International Conference on Computer Modelling and Simulation. 3--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. S. Pandey, L. L. Wu, S. M. Guru, and R. Buyya. 2010. 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 Networks and Applications. 400--407. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. C. Papagianni, A. Leivadeas, S. Papavassiliou, V. Maglaris, C. Cervello-Pastor, and A. Monje. 2013. On the optimal allocation of virtual resources in cloud computing networks. IEEE Transactions on Computers 62, 6 (2013), 1060--1071. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. D. H. Phan, J. Suzuki, R. Carroll, S. Balasubramaniam, W. Donnelly, and D. Botvich. 2012. Evolutionary multiobjective optimization for green clouds. In Proceedings of the Genetic and Evolutionary Computation Conference 19--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. J. J. Rao and K. V. Cornelio. 2012. An optimised resource allocation approach for data-intensive workloads using topology-aware resource allocation. In Proceedings of the IEEE International Conference on Cloud Computing in Emerging Markets. 1--4.Google ScholarGoogle Scholar
  77. M. A. Rodriguez and R. Buyya. 2014. Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Transactions on Cloud Computing 2, 2 (2014), 222--235.Google ScholarGoogle ScholarCross RefCross Ref
  78. V. Roberge, M. Tarbouchi, and G. Labonte. 2013. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics 9, 1 (2013), 132--141.Google ScholarGoogle ScholarCross RefCross Ref
  79. M. A. Sharkh, M. Jammal, A. Shami, and A. Ouda. 2013. Resource allocation in a network-based cloud computing environment: Design challenges. IEEE Communications Magazine 51, 11 (2013), 46--52.Google ScholarGoogle ScholarCross RefCross Ref
  80. G. Shen and Y. Q. Zhang. 2011. A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. In Proceedings of the International Conference on Advances in Swarm Intelligence. Lecture Notes in Computer Science, Volume 6728. Springer, Berlin, 522--529. Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. M. Shen, Z. H. Zhan, W. N. Chen, Y. J. Gong, J. Zhang, and Y. Li. 2014. Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Transactions on Industrial Electronics 61, 12 (2014), 7141--7151.Google ScholarGoogle ScholarCross RefCross Ref
  82. B. Song, M. M. Hassan, E. N. Huh, C. W. Yoon, and H. W. Lee. 2009. A hybrid algorithm for partner selection in market oriented cloud computing. In Proceedings of the International Conference on Management and Service Science. 1--4.Google ScholarGoogle Scholar
  83. B. Speitkamp and M. Bichler. 2010. A mathematical programming approach server consolidation problems in virtualized data centers. IEEE Transactions on Services Computing 3, 4 (2010), 266--278. Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. C. Szabo and T. Kroeger. 2012. Evolving multi-objective strategies for task allocation of scientific workflows on public clouds. In Proceedings of the IEEE World Congress on Computation Intelligence. 1--8.Google ScholarGoogle Scholar
  85. K. C. Tan, A. Tay, and J. Cai. 2003. Design and implementation of a distributed evolutionary computing software. IEEE Transactions on Systems, Man, and Cybernetics 33, 3 (2003), 325--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. M. Tang and S. Pan. 2014. A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Processing Letters, DOI: 10.1007/s11063-014-9339-8, 1--11 Google ScholarGoogle ScholarCross RefCross Ref
  87. M. Tang and Z. I. M. Yusoh. 2012. A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in cloud computing. Parallel Problem Solving from Nature, Lecture Notes in Computer Science, Volume 7492. Springer-Verlag, Berlin, 225--234. Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. F. Tao, Y. Feng, L. Zhang, and T. W. Liao. 2014. CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Applied Soft Computing 19 (2014), 264--279.Google ScholarGoogle ScholarCross RefCross Ref
  89. K. M. Tolle, D. Tansley, and A. J. G. Hey. 2011. The fourth paradigm: Data-intensive scientific discovery. Proceedings of the IEEE 99, 8 (2011), 1334--1337.Google ScholarGoogle ScholarCross RefCross Ref
  90. A. N. Toosi, R. N. Calheiros, and R. Buyya. 2014. Interconnected cloud computing environments: Challenges, taxonomy, and survey. ACM Computing Surveys 47, 1 (2014), 1--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  91. D. Tsoumakos, I. Konstantinou, C. Boumpouka, S. Sioutas, and N. Koziris. 2013. Automated, elastic resource provisioning for NoSQL clusters using TIRAMOLA. In Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 34--41.Google ScholarGoogle Scholar
  92. H. N. Van, F. D. Tran, and J. Menaud. 2010. Performance and power management for cloud infrastructures. In Proceedings of the IEEE 3rd International Conference on Cloud Computing. 329--336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  93. X. L. Wang, Y. P. Wang, and H. Zhu. 2012. Energy-efficient multi-job scheduling model for cloud computing and its genetic algorithm. Mathematical Problems in Engineering, Article ID 589243, 1--16.Google ScholarGoogle Scholar
  94. X. T. Wen, M. H. Huang, and J. H. Shi. 2012. Study on resources scheduling based on ACO algorithm and PSO algorithm in cloud computing. In Proceedings of the 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science. 219--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. H. J. Wu and S. H. Chen. 2011. Cloud database resource calculations optimization based on buzzers and genetic algorithm double-population evolution mechanism. In Proceedings of the Cross Strait Quad-Regional Radio Science and Wireless Technology Conference. 1188--1192.Google ScholarGoogle Scholar
  96. Z. J. Wu, X. Liu, Z. W. Ni, D. Yuan, and Y. Yang. 2013. A market-oriented hierarchical scheduling strategy in cloud workflow systems. Journal of Supercomputing 63, 1 (2013), 256--293.Google ScholarGoogle ScholarCross RefCross Ref
  97. Z. J. Wu, Z. W. Ni, L. C. Gu, and X. Liu. 2010. A revised discrete particle swarm optimization for cloud workflow scheduling. In Proceedings of the International Conference on Computational Intelligence and Security. 184--188. Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Z. Xiao, W. Song, and Q. Chen. 2013. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems 24, 6 (2013), 1107--1117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. F. Xu, F. M. Liu, H. Jin, and A. V. Vasilakos. 2014. Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions. Proceedings of the IEEE 102, 1 (2014), 11--31.Google ScholarGoogle ScholarCross RefCross Ref
  100. J. L. Xu, J. Tang, K. Kwiat, W. Y. Zhang, and G. L. Xue. 2013. Enhancing survivability in virtualized data centers: A service-aware approach. IEEE Journal on Selected Areas in Communications 31, 12 (2013), 2610--2619.Google ScholarGoogle ScholarCross RefCross Ref
  101. Z. Ye, X. F. Zhou, and A. Bouguettaya. 2011. Genetic algorithm based QoS-Aware service compositions in cloud computing. Database Systems for Advanced Applications, Lecture Notes in Computer Science, Volume 6588. Springer, Berlin, 321--334. Google ScholarGoogle ScholarDigital LibraryDigital Library
  102. W. C. Yeh, Y. M. Yeh, and L. M. Lin. 2012. The application of bi-level programming with Stackelberg equilibrium in cloud computing based on simplified swarm optimization. In Proceedings of the 8th International Conference on Computing Technology and Information Management. 809--814.Google ScholarGoogle Scholar
  103. X. D. You, X. H. Xu, J. Wan, D. J. Yu. 2009. RAS-M: Resource allocation strategy based on market mechanism in cloud computing. In Proceedings of the 4th Annual ChinaGrid Conference. 256--263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  104. J. Yu, R. Buyya, and K. Ramamohanarao. 2008. Workflow scheduling algorithms for grid computing. Metaheuristics for Scheduling in Distributed Computing Environments Studies in Computational Intelligence 146 (2008), 173--214.Google ScholarGoogle Scholar
  105. W. J. Yu, M. Shen, W. N. Chen, Z. H. Zhan, Y. J. Gong, Y. Lin, O. Liu, and J. Zhang. 2014. Differential evolution with two-level parameter adaptation. IEEE Transactions on Cybernetics 44, 7 (2014), 1080--1099.Google ScholarGoogle ScholarCross RefCross Ref
  106. B. W. Yuan and S. C. Wu. 2012. An adaptive simulated annealing genetic algorithm for the data placement problem in SAAS. In Proceedings of the International Conference on Industrial Control and Electronics Engineering. 1037--1043. Google ScholarGoogle ScholarDigital LibraryDigital Library
  107. Z. I. M. Yusoh and M. Tang. 2010a. A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud. In Proceedings of the IEEE Congress on Evolutionary Computation. 1--8.Google ScholarGoogle Scholar
  108. Z. I. M. Yusoh and M. Tang. 2010b. A cooperative coevolutionary algorithm for the composite SaaS placement problem in the cloud. Neural Information Processing: Theory and Algorithms, Lecture Notes in Computer Science, Volume 6443. Springer, Berlin, 618--625. Google ScholarGoogle ScholarDigital LibraryDigital Library
  109. Z. I. M. Yusoh and M. Tang. 2012a. Composite SaaS placement and resource optimization in cloud computing using evolutionary algorithms. In Proceedings of the IEEE 5th International Conference on Cloud Computing. 590--597. Google ScholarGoogle ScholarDigital LibraryDigital Library
  110. Z. I. M. Yusoh and M. Tang. 2012b. Clustering composite SaaS components in cloud computing using a grouping genetic algorithm. In Proceedings of the IEEE World Congress on Computational Intelligence. 1--8.Google ScholarGoogle Scholar
  111. S. Zaman and D. Grosu. 2013. A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds. IEEE Transactions on Cloud Computing 1, 2 (2013), 129--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. Z. H. Zhan, J. Li, J. Cao, J. Zhang, H. Chung, and Y. H. Shi. 2013. Multiple populations for multiple objectives: A coevolutionary technique for solving multiobjective optimization problems. IEEE Transactions on Cybernetics 43, 2 (2013), 445--463.Google ScholarGoogle ScholarCross RefCross Ref
  113. Z. H. Zhan, G. Y. Zhang, Y. Lin, Y. J. Gong, and J. Zhang. 2014. Load balance aware genetic algorithm for task scheduling in cloud computing. In Simulated Evolution and Learning, Lecture Notes in Computer Science, Volume 8886, 644--655.Google ScholarGoogle Scholar
  114. Z. H. Zhan and J. Zhang. 2010. Self-adaptive differential evolution based on PSO learning strategy. In Proceedings of the Genetic and Evolutionary Computation Conference. 39--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung. 2009. Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics 39, 6 (2009), 1362--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  116. Z. H. Zhan, J. Zhang, Y. Li, and Y. H. Shi. 2011. Orthogonal learning particle swarm optimization. IEEE Transactions on Evolutionary Computation 15, 6 (2011), 832--847.Google ScholarGoogle ScholarCross RefCross Ref
  117. Z. H. Zhan, J. Zhang, Y. H. Shi, and H. L. Liu. 2012. A modified brain storm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation. 1--8.Google ScholarGoogle Scholar
  118. F. Zhang, J. Cao, K. Hwang, and C. Wu. 2011a. Ordinal optimized scheduling of scientific workflows in elastic compute clouds. In Proceedings of the IEEE International Conference on Cloud Computing Technology and Science. 9--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  119. F. Zhang, J. Cao, W. Tan, S. U. Khan, K. Li, and A. Y. Zomaya. 2014a. Evolutionary scheduling of dynamic multitasking workloads for big-data analytics in elastic cloud. IEEE Transactions on Emerging Topics in Computing 2, 3 (2014), 338--351.Google ScholarGoogle ScholarCross RefCross Ref
  120. M. D. Zhang, Z. H. Zhan, J. J. Li, and J. Zhang. 2014b. Tournament selection based artificial bee colony algorithm with elitist strategy. In Proceedings of the Conference on Technologies and Applications of Artificial Intelligence, 387--396.Google ScholarGoogle Scholar
  121. Q. Zhang, L. Cheng, and R. Boutaba. 2010. Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications 1, 1 (2010), 7--18.Google ScholarGoogle ScholarCross RefCross Ref
  122. Y. H. Zhang, L. Feng, and Z. Yang. 2011b. Optimization of cloud database route scheduling based on combination of genetic algorithm and ant colony algorithm. Procedia Engineering 15 (2011), 3341--3345.Google ScholarGoogle ScholarCross RefCross Ref
  123. J. Zhang, Z. H. Zhan, Y. Lin, N. Chen, Y. J. Gong, J. H. Zhong, H. Chung, Y. Li, and Y. H. Shi. 2011c. Evolutionary computation meets machine learning: A survey. IEEE Computational Intelligence Magazine 6, 4 (2011), 68--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  124. Z. H. Zhang and X. J. Zhang. 2010. A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In Proceedings of the 2nd International Conference on Industrial Mechatronics and Automation. 240--243.Google ScholarGoogle Scholar
  125. C. H. Zhao, S. S. Zhang, Q. F. Liu, J. Xie, and J. C. Hu. 2009. Independent tasks scheduling based on genetic algorithm in cloud computing. In Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing, 1--4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  126. J. F. Zhao, W. H. Zeng, M. Liu, and G. M. Li. 2011. Multi-objective optimization model of virtual resources scheduling under cloud computing and its solution. In Proceedings of the International Conference on Cloud and Service Computing. 185--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  127. H. Zhong, K. Tao, and X. J. Zhang. 2010. An approach to optimized resource scheduling algorithm for open-source cloud systems. In Proceedings of the 5th Annual ChinaGrid Conference. 124--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  128. L. Zhou, Y. C. Wang, J. L. Zhang, J. Wan, and Y. J. Ren. 2012. Optimize block-level cloud storage system with load-balance strategy. In Proceedings of the IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum. 2162--2167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  129. L. N. Zhu, Q. S. Li, and L. N. He. 2012. Study on cloud computing resource scheduling strategy based on the ant colony optimization algorithm. International Journal of Computer Science Issues 9, 5 (2012), 54--58.Google ScholarGoogle Scholar
  130. K. Zhu, H. G. Song, L. J. Liu, J. Z. Gao, and G. J. Cheng. 2011. Hybrid genetic algorithm for cloud computing applications. In Proceedings of the IEEE Asia-Pacific Services Computing Conference. 182--187.Google ScholarGoogle Scholar

Index Terms

  1. Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

                                Recommendations

                                Comments

                                Login options

                                Check if you have access through your login credentials or your institution to get full access on this article.

                                Sign in

                                Full Access

                                • Published in

                                  cover image ACM Computing Surveys
                                  ACM Computing Surveys  Volume 47, Issue 4
                                  July 2015
                                  573 pages
                                  ISSN:0360-0300
                                  EISSN:1557-7341
                                  DOI:10.1145/2775083
                                  • Editor:
                                  • Sartaj Sahni
                                  Issue’s Table of Contents

                                  Copyright © 2015 ACM

                                  Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                                  Publisher

                                  Association for Computing Machinery

                                  New York, NY, United States

                                  Publication History

                                  • Published: 21 July 2015
                                  • Accepted: 1 May 2015
                                  • Revised: 1 March 2015
                                  • Received: 1 July 2014
                                  Published in csur Volume 47, Issue 4

                                  Permissions

                                  Request permissions about this article.

                                  Request Permissions

                                  Check for updates

                                  Qualifiers

                                  • survey
                                  • Research
                                  • Refereed

                                PDF Format

                                View or Download as a PDF file.

                                PDF

                                eReader

                                View online with eReader.

                                eReader