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All Journal Journal of Renewable Energy, Electrical, and Computer Engineering
Mayanda, Deara
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Load Balancing Techniques for Server Clustering in Cloud Environment: Systematic Literature Review Mayanda, Deara; Amaliah, Annisa Rizki; Raharja, Muhammad Ridwan Ali; Nurbojatmiko, Nurbojatmiko
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 4 No. 2 (2024): September 2024
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v4i2.14906

Abstract

The rapid development of cloud computing has a significant impact on increasing the workload on resources, which is often excessive and a major challenge in computing environments.  Load balancing is key to avoid overloading or underloading virtual machines, given the high user demand for service availability. There are several types of load balancing techniques, and this diversity poses its own challenges in selecting the optimal technique to address workload issues. This research presents a systematic literature review with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify various load balancing techniques for server clustering in a cloud computing environment. The purpose of this research is to review previous research on load balancing techniques for server clustering in cloud computing by categorizing based on problems, solutions, research methods, objects, and research results. Research that uses the experimental method will be reviewed again to categorize the research results based on the load balancing matrix, namely response time, make span, resource utilization, migration time, fault tolerance, throughput, and cost. Various publishers, such as IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi were explored as data sources. The research conducted generates more information about load balancing techniques for clustering servers in cloud computing and allows other researchers to fill the current research gap.
Co-Authors Amaliah, Annisa Rizki Nurbojatmiko Nurbojatmiko, Nurbojatmiko Raharja, Muhammad Ridwan Ali