Svoboda | Graniru | BBC Russia | Golosameriki | Facebook

survey

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Authors:

Henry Shu-Hung Chung,

Yun LiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 47, Issue 4

Article No.: 63, Pages 1 - 33

https://doi.org/10.1145/2788397

Published: 21 July 2015 Publication History

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.

Digital 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.

[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.

[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.

Digital 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.

[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.

Digital 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.

[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.

[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.

[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.

Digital 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.

Digital 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.

Digital 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.

[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.

[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.

Digital 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.

[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.

[18]

L. Chimakurthi and M. Kumar. 2011. Power efficient resource allocation for clouds using ant colony framework. Arxiv preprint arXiv:1102.2608, 1--6.

[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.

[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.

[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.

Digital 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.

[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.

[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.

Digital Library

[25]

A. Dragland. 2013. Big data, for better or worse: 90&percnt; of world's data generated over last two years. Science Daily (May 2013).

[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.

Digital 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.

Digital 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.

Digital 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.

[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.

Digital 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.

[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.

Digital 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.

[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.

[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.

[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.

Digital 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.

[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.

Digital 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.

Digital 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.

[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.

Digital 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.

[43]

B. Jennings and R. Stadler. 2014. Resource management in clouds: Survey and research challenges. Journal of Network Systems Management 1--53.

Digital 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.

[45]

H. Kaur and M. Singh. 2012. Review of various scheduling techniques in cloud computing. International Journal of Networking & Parallel Computing 1, 2 (2012).

[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.

[47]

T. S. Kuhn. 2012. The Structure of Scientific Revolutions, University of Chicago Press.

[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.

[49]

S. Kumar and P. Balasubramanie. 2012. Dynamic scheduling for cloud reliability using transportation problem. Journal of Computer Science 8, 10 (2012), 1615--1626.

[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.

[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.

Digital 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.

[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.

[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.

Digital 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.

Digital 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.

Digital 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.

Digital 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.

[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.

[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.

[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.

[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.

Digital 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.

[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.

Digital 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.

[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.

[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.

Digital 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.

Digital 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.

[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.

[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.

Digital 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.

Digital 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.

Digital 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.

Digital 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.

Digital 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.

[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.

[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.

[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.

[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.

Digital 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.

[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.

[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.

Digital 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.

[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.

Digital 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. 1--11

[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.

Digital 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.

[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.

[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.

Digital 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.

[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.

Digital 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.

[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.

Digital 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.

[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.

[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.

Digital 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.

Digital 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.

[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.

[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.

Digital 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.

[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.

Digital 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.

[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.

[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.

Digital 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.

[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.

Digital 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.

Digital 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.

[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.

Digital 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.

[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.

[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.

Digital 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.

Digital 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.

[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.

[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.

Digital 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.

[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.

[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.

[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.

[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.

Digital 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.

[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.

Digital 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.

Digital 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.

Digital 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.

Digital 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.

[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.

Cited By

Dong GLi WDong ZWang CQian SZhang TMa XZou LLin KLiu Z(2024)Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer ModelApplied Sciences10.3390/app1404165714:4(1657)Online publication date: 19-Feb-2024
https://doi.org/10.3390/app14041657
Rana NJeribi FKhan ZAlrawagfeh WBen Dhaou IHaseebuddin MUddin M(2024)A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computingFrontiers in Computer Science10.3389/fcomp.2024.12885526Online publication date: 8-Jul-2024
https://doi.org/10.3389/fcomp.2024.1288552
Kızıl AKarabulut K(2024)Bulut sistemlerinde toplam tamamlanma ve enerji tabanlı sanal makine çizelgelemesiGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.120233639:3(1661-1672)Online publication date: 20-May-2024
https://doi.org/10.17341/gazimmfd.1202336
Show More Cited By

Index Terms

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Recommendations

Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
CIBDA '24: Proceedings of the 5th International Conference on Computer Information and Big Data Applications

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of ...
Energy optimised resource scheduling algorithm for private cloud computing

In universities, IT infrastructure is usually non-centralised. Setting up a private cloud setup in academic institutes has an edge over the traditional approach of IT infrastructure distribution to its various departments, schools and centres etc. This ...
Cloud resource provisioning: survey, status and future research directions

Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery ...

Comments

Information & Contributors

Information

Published In

ACM Computing Surveys Volume 47, Issue 4

July 2015

573 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2775083

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering/University of Florida/Gainesville, FL

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: 01 May 2015

Revised: 01 March 2015

Received: 01 July 2014

Published in CSUR Volume 47, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Research
Refereed

Funding Sources

the NSFC for Distinguished Young Scholars
the NSFC Key Program
the National Natural Science Foundations of China (NSFC)
the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars
the Project for Pearl River New Star in Science and Technology
the Fundamental Research Funds for the Central Universities
the National High-Technology Research and Development Program (863 Program) of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

372
Total Citations
View Citations
5,233
Total Downloads

Downloads (Last 12 months)334
Downloads (Last 6 weeks)21

Reflects downloads up to 25 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Dong GLi WDong ZWang CQian SZhang TMa XZou LLin KLiu Z(2024)Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer ModelApplied Sciences10.3390/app1404165714:4(1657)Online publication date: 19-Feb-2024
https://doi.org/10.3390/app14041657
Rana NJeribi FKhan ZAlrawagfeh WBen Dhaou IHaseebuddin MUddin M(2024)A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computingFrontiers in Computer Science10.3389/fcomp.2024.12885526Online publication date: 8-Jul-2024
https://doi.org/10.3389/fcomp.2024.1288552
Kızıl AKarabulut K(2024)Bulut sistemlerinde toplam tamamlanma ve enerji tabanlı sanal makine çizelgelemesiGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.120233639:3(1661-1672)Online publication date: 20-May-2024
https://doi.org/10.17341/gazimmfd.1202336
Nyakio MWere MWekesa CLungayia HOkoth PWere H(2024) Molecular Footprints of Potato Virus Y Isolate Infecting Potatoes ( Solanum tuberosum ) in Kenya Advances in Virology10.1155/2024/21977252024:1Online publication date: 6-Aug-2024
https://doi.org/10.1155/2024/2197725
Rajnoha RHadač J(2024)Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research FrameworkIEEE Transactions on Engineering Management10.1109/TEM.2021.311350271(90-105)Online publication date: 2024
https://doi.org/10.1109/TEM.2021.3113502
Cortellessa VDi Pompeo DTucci M(2024)Exploring Sustainable Alternatives for the Deployment of Microservices Architectures in the Cloud2024 IEEE 21st International Conference on Software Architecture (ICSA)10.1109/ICSA59870.2024.00012(34-45)Online publication date: 4-Jun-2024
https://doi.org/10.1109/ICSA59870.2024.00012
Janet Kumari JS TRaja A(2024)QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)10.1109/ICICV62344.2024.00114(685-691)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICICV62344.2024.00114
Jain ANath MRama Parvathy L(2024)Optimising Makespan Minimization and Performance at the Same Time with a Dynamic Cloud Task Scheduling Comparative Study of MOSOS and PSO2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)10.1109/IC2PCT60090.2024.10486252(944-949)Online publication date: 9-Feb-2024
https://doi.org/10.1109/IC2PCT60090.2024.10486252
Liu SChen CQu XTang KOng Y(2024)Large Language Models as Evolutionary Optimizers2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10611913(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10611913
Joshi KKumar RBharany SSaini DKumar RIbrahim AAbdelmaboud ANagmeldin WMedani M(2024)Exploring the Connectivity Between Education 4.0 and Classroom 4.0: Technologies, Student Perspectives, and Engagement in the Digital EraIEEE Access10.1109/ACCESS.2024.335778612(24179-24204)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3357786
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents