Volume 11 - Volume 11
Adaptive Task Partitioning for Performance Evaluation in Cluster based Heterogeneous Environments
Abstract
Due to continual server replacement, datacenter-scale clusters are developing toward heterogeneous
hardware designs. Meanwhile, datacenters are frequently used by a variety of users for a variety of
purposes. Due to multi-tenant interferences, it frequently exhibits high performance heterogeneity.
When contrasted to in-house dedicated clusters, deploying MapReduce on such heterogeneous
clusters poses major hurdles in attaining adequate application performance. Heterogeneity can cause
significant performance degradation in job execution, despite current optimizations on task scheduling and load balancing, because most MapReduce implementations were developed for
homogeneous contexts. To make scheduling decisions, the majority of extant adaptive strategies
assume a priori knowledge of particular job characteristics. However, without spending a significant
cost, such information is not readily available. The suggested framework Adaptive Control Self-tuning provides a significant improvement over existing methods at moderate to high system
utilizations, according to the evaluation results.
Paper Details
PaperID: 2351
Author's Name: Gosula Anitha, Dr.K. Vengatesan and Dr. Neeraj Sharma
Volume: Volume 11
Issues: Volume 11
Keywords: MapReduce, Cluster, Heterogeneous Systems, Adaptive Task Tuning.
Year: 2021
Month: July
Pages: 3080-3088