Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Hybrid Cloud Environment using Map reduce for Effective Job Scheduling

Author : G.Suhasini 1 Dr. P.Niranjan 2

Date of Publication :12th October 2017

Abstract: MapReduce plays major role in data intensive applications. Hadoop MapReduce is wide used framework across the world also used for data-intensive applications that require exploiting data processing power of distributed programming frameworks. One of the great options of Hadoop MapReduce is its support for cloud organizations. Many service providers like Amazon with Elastic MapReduce are having provision for running Hadoop applications in this context, it's essential to have effective job planning and resource provisioning mechanisms for public cloud. Khan et al. proposed Hadoop Performance Modelling for job estimation and resource provisioning to improve user satisfaction besides helping users to have optimal utilization of cloud resources. This paper proposed effective job scheduling and resource provisioning The users are made responsible to make decisions on resource needs. Often the users are unaware of the resource requirements. This proposed methodology has provision for estimating the execution time of given job which has deadline requirements. In a hybrid cloud environment, the estimation of time is made. This can help in automating resource provision and job scheduling to ensure that job is execution in the given deadline.

Reference :

Will Updated soon

Recent Article