Dynamic Resource Allocation for Parallel Data Processing in Cloud Computing Environment

Abstract

Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research community in the last few years. Many researchers around the world have come up with new ways of facing this challenge. Ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud provider companies has started to include frameworks for parallel data processing in their product which making it easy for customers to access these services and to deploy their programs. The processing frameworks which are currently used have been designed for static and homogeneous cluster setups. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase processing cost and time. Again due to opaque nature of cloud, static allocation of resources is possible, but vice-versa in dynamic situations. The proposed new Generic data processing framework is intended to explicitly exploit the dynamic resource allocation in cloud for task scheduling and execution.