Task scheduling in cloud environment: A multi-objective ABC framework

References

• Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia, Above the clouds: A berkeley view of cloud computing,” UCB/EECS-2009-28, 2009.
   Google Scholar
• J. Dean and S. Ghemawat, MapReduce: simplified data processing on large clusters, Sixth Symposium on Operating System Design and Implementation (OSDI’04), Dec. 2004, pp. 1-13.
   Google Scholar
• Rajkumar Buyyaa, Chee Shin Yeo, Srikumar Venugopal, James Bro-berg, Ivona Brandic, Cloud Computing and emerging IT Platforms: Vision, Hype, and reality for delivering Computing as the 5th Utility, Journal of Future Generation Computer Systems, Vol.25(6)(2009). doi: 10.1016/j.future.2008.12.001
   PubMed Web of Science ®Google Scholar
• Liu, K., Scheduling Algorithms for Instance Intensive Cloud Work-flows, Ph.D. Thesis, Swinburne University of Technology, Australia, 2009.
   Google Scholar
• LI Wenhao, A Community Cloud Oriented Workflow System Frame-work and its Scheduling Strategy, IEEE 2nd Symposium on Web Society (SWS), 2010.
   Google Scholar
• Leavitt N, Is Cloud Computing Really Ready for Prime Time?, Computer, Vol. 42 (2009), pp. 15-20.
   Web of Science ®Google Scholar
• Weinhardt C, Anandasivam A, Blau B, and Stosser J, Business Models in the Service World, IT Professional, Vol. 11(2009), pp. 28-33. doi: 10.1109/MITP.2009.21
   Google Scholar
• Zhangjun, W., Xiao, L., Zhiwei, N., Dong, Y., and Yun, Y., A Market-Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems, Journal of Supercomputing, 2011.
   Web of Science ®Google Scholar
• A. Y. Zomaya, and Y. The, Observations on using genetic algorithms for dynamic load-balancing, IEEE Transaction on Parallel and Distributed Systems, Vol. 12(9)(2001), pp. 899-911 doi: 10.1109/71.954620
   Web of Science ®Google Scholar
• John C. Mace, Aad Van Moorsel, Paul Watson, The Case for Dynamic Security Solutions in Public Cloud Workflow Deployments, Proceeding of the IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops, 2011.
   Google Scholar
• Bhaskar Prasad Rimal, Mohamed A. El-Refaey, A Framework of Scientific Workflow Management Systems for Multi-Tenant Cloud Orchestration Environment, Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2010.
   Google Scholar
• Ke, L., Hai, J., Jinjun, C., Xiao, L., Dong, Y., and Yun, Y, A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on Cloud Computing Plat-form, International Journal of High Performance Computing Applications, Vol-8(2010), pp.1-16.
   Google Scholar
• S.Sindhu, and S. Mukherjee, Efficient task scheduling algorithms for cloud computing environment, in High Performance Architecture and Grid Computing, Communications in Computer and Information Science, 2011, vol. 169(2011), pp. 79-83. doi: 10.1007/978-3-642-22577-2_11
   Google Scholar
• S. T. Maguluri, R. Srikant, and L. Ying, Stochastic models of load balancing and scheduling in cloud computing clusters, in Proc. IEEE Infocom, pp. 702-710, 2012.
   Google Scholar
• Y. C. Hsu, P. Liu, and J. J. Wu, Job sequence scheduling for cloud computing, In Int. Conf. on Cloud and Service Computing (CSC 2011), , pp. 212-219, Dec. 2011.
   Google Scholar
• Y. Fang, F. Wang, and J. Ge, A task scheduling algorithm based on load balancing in cloud computing, in Web Information Systems and Mining, Lecture Notes in Computer Science 2010, pp. 271-277, 2010.
   Google Scholar
• R. N. Calheiros, R.Ranjan, C. A. F. D. Rose, and R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software : Practice and Experience, Vol. 41(1)(2011), pp. 23-50.
   Web of Science ®Google Scholar
• R. N. Calheiros, R.Ranjan, C. A. F. D. Rose, and R. Buyya, CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services, arXiv preprint arXiv: 0903.2525, 2009.
   Google Scholar
• B. Mondal, K. Dasgupta, and P. Dutta, Load balancing in cloud computing using Stochastic Hill Climbimg-A soft computing approach, In Procedia Tachnology, Vol. 4, pp. 783-789, 2011. doi: 10.1016/j.protcy.2012.05.128
   Google Scholar
• J. Hu, J. Gu, G. Sun, and T. Zhao, A scheduling strategy on load balancing of virtual machine resources in cloud computing environment, In 3rd Int. Symp. on Parallel Architectures, Algorithms and Programming(PAAP-2010), , pp. 89-96, 18-20 Dec. 2010.
   Google Scholar
• Y. Wei, and L. Tian. Research on cloud design resources scheduling based on genetic algorithm, In 2012 Int. Conf. on Systems and Informatics (ICSAI 2012), pp. 2651-2656, May 2012.
   Google Scholar
• K. Li, G. Xu, G. Zhao, Y. Dong, and D. Wang, Cloud task scheduling based on load balancing Ant Colony Optimization, In 6th Annual ChinaGrid Conf., 2011, , pp. 3-9, Aug. 2011.
   Google Scholar
• D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, A comprehensive survey: artificial bee colony (ABC) algorithm and applications, In Artificial Intelligence Review 2012, Springer Science Business Media B.V. 2012, March 2012.
   Google Scholar
• S. Bitam, Bees life algorithm for job scheduling in cloud computing, In Conf. on Computing and Information Technology (ICCIT 2012), pp. 186-191, 2012.
   Google Scholar
• T. Mizan, S. M. R. A. Masud, and R. Latip, Modified bees life algorithm for job scheduling in hybrid cloud, Int. Journal of Engineering and Technology(IJET), 2012, Vol. 2(6)(2012), pp. 974-979.
   Google Scholar
• R K Jena, Multi-objective Task Scheduling in Cloud Environment Using Nested PSO Framework, Procedia Computer Science, Vol.57 (2015), pp. 1219–1227 , 2015. doi: 10.1016/j.procs.2015.07.419
   Google Scholar
• D. Karaboga, An idea based on honey bee swarm for numerical optimization, Tech. Rep. TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
   Google Scholar
• D. Karaboga and B. Basturk, On the performance of artificial bee colony _ABC_ algorithm, Applied Soft Computing Journal, Vol. 8(1)(2008), pp. 687–697. doi: 10.1016/j.asoc.2007.05.007
   Web of Science ®Google Scholar
• D. Karaboga and B. Akay, Artificial Bee Colony _ABC_ Algorithm on training artificial neural networks, In Proceedings of the IEEE 15th Signal Processing and Communications Applications (SIU ‘07), June 2007.
   Google Scholar
• R K Jena, Artificial Bee Colony Algorithm based Multi-Objective Node Placement for Wireless Sensor Network, International Journal of Information Technology and Computer Science (IJITCS). 6(6)(2014), pp. 25-32. doi: 10.5815/ijitcs.2014.06.04
   Google Scholar
• K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6 (2)(2002), pp. 182–197. doi: 10.1109/4235.996017
   Web of Science ®Google Scholar