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

Abstract

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.

Inject an Elastic Grid Computing Techniques to Optimal Resource Management Technique Operations

“…Access the wireless resources via resource providers, simultaneously from the grid portal for a job (Singh, 2011;Singh et al, 2013). Existing systems was not containing the inclusive disjunction model, so not efficient to get the cleaned data and normal data.…”