An Intelligent Job Scheduling System in Cloud Computing

Abstract:

Cloud computing is a new computing and business paradigm with flexible and powerful computational architecture to offer universal services to users via Internet. The performance of the scheduling system influences the cost benefit of this computing paradigm. Thus, jobs should be scheduled efficiently to reduce the execution cost and time. In this paper, we present an intelligent scheduling system, which considers both the requirements of different service requests and the circumstances of the computing infrastructure which consists of various resource, then, the main components of the system are introduced in detail, at last, the conclusions are drawn and the further research directions of the scheduling systems are pointed out.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1391-1394
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1391
• Online since: February 2013
• Authors: Jing Liu, Xing Guo Luo, Bai Nan Li
• Keywords: Cloud Computing, Cognitive Decision, Intelligent Job Scheduling, Resource Allocation, Web Service

References

[1] Armbrust M, Fox A, Griffith R, Joseph A D, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A and Stoica I: A view of cloud computing. Communications of the ACM, Vol. 53, Issue 4 (2010), pp.50-58.

DOI: 10.1145/1721654.1721672

[2] Mell P and Grance T: The NIST definition of cloud computing. Communications of the ACM, Vol. 53, Issue 6 (2010), pp.50-58.

[3] L. Shyamala and Saswati Mukherjee: EduCloud: An Institutional Cloud with Optimal Scheduling Policies, ObCom 2011, Part I, CCIS 269 (2012), pp.114-123.

DOI: 10.1007/978-3-642-29219-4_14

[4] P. Padala, K.Y. Hou, K.G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant: Automated control of multiple virtualized resources, In Proceedings of the 4th ACM European conference on Computer systems (2009), pp.13-26.

DOI: 10.1145/1519065.1519068

[5] G. Tesauro, N.K. Jong, R. Das, and M.N. Bennani: On the use of hybrid reinforcement learning for autonomic resource allocation, Cluster Computing, Vol. 10, Issue 3 (2007), pp.287-299.

DOI: 10.1007/s10586-007-0035-6

[6] J. Rao, X. Bu, C.Z. Xu, L. Wang, and G. Yin: VCONF: a reinforcement learning approach to virtual machines auto-configuration, In Proceedings of the 6th international conference on Autonomic computing, ACM (2009), pp.137-146.

DOI: 10.1145/1555228.1555263

[7] Afzal, A., A.S. McGough, and J. Darlington: Capacity planning and scheduling in Grid computing environments, Future Generation Computer Systems, Vol 24, Issue 5 (2008), pp.404-414.

DOI: 10.1016/j.future.2007.07.004

[8] Yuan-Shun, D., X. Min, and P. Kim-Leng: Availability Modeling and Cost Optimization for the Grid Resource Management System, Systems, Man and Cybernetics, Part A, IEEE Transactions on, Vol. 38, Issue 1(2008), pp.170-179.

DOI: 10.1109/tsmca.2007.909546