A Systematic Mapping Study of Cloud Resources Management and Scalability in Brokering, Scheduling, Capacity Planning and Elasticity

Odun-Ayo, Isaac and Ajayi, Olasupo and Goddy-Worlu, Rowland and Yahaya, Jamaiah (2019) A Systematic Mapping Study of Cloud Resources Management and Scalability in Brokering, Scheduling, Capacity Planning and Elasticity. Asian Journal of Scientific Research. ISSN 19921454

PDF Download (583Kb)

Abstract

Cloud computing allows for resource management through various means. Some of these include brokering, scheduling, elasticity and capacity planning and these processes helps in facilitating service utilization. Determining a particular research area especially in terms of resources management and scalability in the cloud is usually a cumbersome process for a researcher, hence the need for reviews and paper surveys in identifying potential research gaps. The objective of this work was to carry out a systematic mapping study of resources management and scalability in the cloud. A systematic mapping study offers a summarized overview of studies that have been carried out in a particular area of interest. It then presents the results of such overviews graphically using a map. Although, the systematic mapping process requires less effort, the results are more coarse-grained. In this study, analysis of publications were done based on their topics, research type and contribution facets. These publications were on research works which focused on resource management, scheduling, capacity planning, scalability and elasticity. This study classified publications into research facets viz., evaluation, validation, solution, philosophical, option and experience and contribution facets based on metrics, tools, processes, models and methods used. Obtained results showed that 31.3% of the considered publications focused on evaluation based research, 19.85% on validation and 32% on processes. About 2.4% focused on metric for capacity planning, 5.6% focused on tools relating to resource management, while 5.6 and 8% of the publications were on model for capacity planning and scheduling method, respectively. Research works focusing on validating capacity planning and elasticity were the least at 2.29 and 0.76%, respectively. This study clearly identified gaps in the field of resources management and scalability in the cloud which should stimulate interest for further studies by both researchers and industry practitioners.

Item Type:	Article
Uncontrolled Keywords:	Cloud computing, elasticity, capacity planning, resource management, scalability, systematic mapping
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	UNSPECIFIED
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	25 Mar 2019 09:29
Last Modified:	25 Mar 2019 09:29
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/12524

Actions (login required)

View Item