Abstract
The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.
Highlights
Cloud computing has emerged as a novel trend in past few years
(d) To propose a novel load balancing (LB) algorithm based on Fault Tolerance (FT) metrics So, the research paper primarily deliberates on LB issues in cloud computing environment
This paper proposes an efficient fault tolerance LB technique that ensures fault tolerance will properly providing multiple objectives: (a) Performance of the system (b) Reduces job make span (c) Deliver effective network (d) Node usage (e) Fulfill III balance load (f) Strong versatility in system (g) Job execution (h) Throughput, (i) Response Time Why Use a Machine Learning Approach to Perform Load Balancing Tasks: The use of ML methods to handle cloud
Summary
Cloud computing has emerged as a novel trend in past few years. It has led to the progression of distributed system to a large scale computing network. (d) To propose a novel LB algorithm based on FT metrics So, the research paper primarily deliberates on LB issues in cloud computing environment. The main aim of the load balancer helps to assign resources to the tasks for resource efficiency and user satisfaction at minimal expense [7], quality output, gripping rapid traffic blast sustain traffic on the website and elasticity which motivates us to identify problems in LB and to work on their resolution [105] This plays a key role in ensuring the ease of access for customers, business partners, and end-users of the cloud-based applications [104].
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