Load balancing in cloud computing: Challenges &amp; issues

Joshi, Shalini; Kumari, Uma

doi:10.1109/ic3i.2016.7917945

Cited by 23 publications

(16 citation statements)

References 10 publications

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“… There is still a limited possibility of having the wrong responses. Threshold Algorithm [4]  Threshold has poor contact with inter process  A variety of allocations are made to local processes.…”

Section: B General Lbmentioning

confidence: 99%

“…It provides a sense of the duration of the dance concerning the existing amount of beehive food & distance. Ant Colony Algorithm [9], [84]  Better LB, greater performance, reduced operation times  ACO has high effectiveness and can thus be utilized frequently in several areas [84]  ACO could be quickly coupled with the other heuristic algorithms [84] Untested in a real cloud environment Genetic Algorithm [22]  Getting better efficiency of the system  Decreasing task times  Better Resource Usage  Tiny throughput  No power saving  Missing the scalability Honey Bee Foraging [9], [93]  Lower makes pan and response time  Good functioning, as the complexity of the system, grows [93]  Does not function for tasks which rely on it  As the size of the machine increases, the throughput doesn't rise [93] Artificial Bee Colony algorithm [28] Minimum migration time  Don't resource utilization  Less security Hybrid (Ant Colony, Honey Bee with Dynamic Feedback [9] Lower response time, better using of resources, fewer task migrations Low complexity, scalability…”

Section: Suffers From Starvationmentioning

confidence: 99%

“…It offers us one of the benefits of keeping data or files in an easy and scalable way and makes them accessible to customers on a large scale. There are many LB algorithms in the cloud system to make the most effective use of resources [4].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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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.

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Section: B General Lbmentioning

confidence: 99%

Section: Suffers From Starvationmentioning

confidence: 99%

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A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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“…But balancing the load for multi-class system is challenging due to complication produced in load data fusion [6].  The major challenge faced in the cloud computing system is to avoid the issues while, the load tends to be dynamically distributed through VM [9].  Several challenges exist in LB algorithms that include security issues, interoperability issues, data management issues, and Quality of Service (QoS) performance issues.…”

Section: Challengesmentioning

confidence: 99%

“…Due to different services, the cloud computing provides simple and flexible mechanisms for keeping data and files to make them accessible for huge scale users [9]. If large amounts of tasks occur on specific node, the tasks can be switched from overloaded nodes to underloaded nodes to minimize the tasks waiting time at nodes which is also termed as Load Balancing (LB).…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Optimization Algorithm for Load Balancing in Cloud

Arora¹,

Dixit²

2019

IJEAT

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The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

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