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Demand-driven Gaussian window optimization for executing preferred population of jobs in cloud clusters

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 3 (2019)

DOI
10.11591/ijece.v9i3.pp1637-1644
ISSN
2088-8708, 2088-8708
Published
2019-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

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.

Collaboration network of the authors

2 authors · 1 co-authorship link · 1 across different organisations

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  • Dayananda Sagar College of Engineering
  • RajaRajeswari College of Engineering
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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