Deploying Efficiently MapReduce Applications in Heterogeneous Computing Environments using Novel Scheduling Algorithms
Abstract
Cloud computing is a new form of Internetbased computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services). Advocates claim that cloud computing allows companies to avoid up-front infrastructure costs (e.g., purchasing servers). As well, it enables organizations to focus on their core businesses instead of spending time and money on computer infrastructure.[4] Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables information technology (IT) teams to more rapidly adjust resources to meet fluctuating and unpredictable business demand. This paper proposes job scheduling algorithms to provide highly efficient job schedulers for the Hadoop system. Job types are not evaluated in the default job scheduling policy of Hadoop, causing some Task Trackers to become overloaded
Keywords
Hadoop, Heterogeneous environments, Heterogeneous workloads, Map Reduce and Scheduling.
Author(s)
Jitendra Joshi, Ashu Sharma, Medhavi Malik
Volume & Issue
Volume :4 , Issue :2
Date of publication
28-April-2018
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