DESIGN OF COMPREHENSIVE FRAMEWORK ON OPTIMIZATION METHODS IN DISTRIBUTED CLUSTERS

Pulamolu, Kiran Kumar and Subramanian, D. Venkata and Krishnaraj, Dr (2018) DESIGN OF COMPREHENSIVE FRAMEWORK ON OPTIMIZATION METHODS IN DISTRIBUTED CLUSTERS. EAI Endorsed Transactions on Energy Web, 5 (20): e15. ISSN 2032-944X

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Abstract

MapReduce is a popular, open source programming paradigm to handle big data which is an industry standard large scale data processing system used by many companies like Yahoo, Google, Facebook, etc. The YARN framework uses low resource fairness algorithms such as FIFO, Capacity, Fair, DRF scheduler, whereas these schedulers are not suitable for heterogeneous Hadoop clusters. Therefore, an Enhanced Combined Regression Ranking (eCRRYARN) algorithm was proposed to enhance resource fairness. The proposed algorithm uses linear regression model to estimate the expected resources to be availed by the tenants. The order ranking is given to the estimated resource and the resources shared as per the ranking provided. Hence, the Hierarchical Hadoop Cluster Resource Sharing (HHCRS) algorithm has been adopted for hierarchical distributed cluster aiming to design a cost effective cluster for organization which is spread across the globe.

Item Type: Article
Uncontrolled Keywords: Distributed Cluster, Resource Fairness, Resource Sharing, Hierarchical Cluster, MapReduce
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 22 Sep 2020 13:54
Last Modified: 22 Sep 2020 13:54
URI: https://eprints.eudl.eu/id/eprint/556

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