Open AccessDemand-driven Gaussian window optimization for executing preferred population of jobs in cloud clusters
Author(s) -
M. Vaidehi,
T. R. Gopalakrishnan
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i3.pp1637-1644
Subject(s) - computer science , cloud computing , provisioning , distributed computing , scheduling (production processes) , latency (audio) , job scheduler , quality of service , population , computer network , mathematical optimization , operating system , telecommunications , mathematics , demography , sociology
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.