A Survey on Application of Machine Learning to Resource Management in Grid Environment

Abstract—Grid represents an environment over a distributed area, incorporating heterogeneous elements such as server nodes, storage devices, and network components in a scalable, wide-area spanning compute infrastructure. Since a Grid requires large scale resource sharing, efficient resource management system (RMS) is required to manage the Quality of Service (QoS). One of the chief tasks of an RMS for Grid is selecting an appropriate and most suitable resource provider for execution of a particular job submitted by a user. This is also known as resource brokering. This paper presents a survey of resource brokering paradigms based on Machine Learning (ML). Three major ML techniques applied in Grid resource management for this purpose have been discussed.

Index Terms—Grid computing, resource management, machine learning, resource brokering, load balancing.

S. Singh, S. Royc, and N. Mukherjee are with the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India (e-mail: susmitasingh09@gmail.com, sarbani.roy@ieee.org, numkherjee@cse.jdvu.ac.in).
M. Sarkar is with the Department of Computer Science and Engineering, Government College of Engineering and Leather Technology, Kolkata, India (e-mail: madhulina.sarkar@gmail.com).

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Cite: Susmita Singh, Madhulina Sarkar, Sarbani Roy, and Nandini Mukherjee, "A Survey on Application of Machine Learning to Resource Management in Grid Environment," Lecture Notes on Software Engineering vol. 1, no. 2, pp. 173-177, 2013.