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@article{144267,
        author = {A.Sheelavathi and N.Radha and J.Sathiaparkavi,V.Gomathi and C. Merlyne Sandra Christina},
        title = { Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {10},
        pages = {13-17},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144267},
        abstract = {Cloud Computing has emerged as the most apt solution for businesses and services because of reduced costs. Software and Hardware have been used as Services in cloud computing. – IAAS [Infrastructure As A Service]. As clouds reduce load on the client both in terms as software or hardware, the server itself is heavily loaded. So it has to be upgraded in terms of hardware resources.  In recent years ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project. It is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution.},
        keywords = {Cloud Computing, Parallel Processing, Increasing Throughput},
        month = {},
        }