Survey of Different Load Balancing Approach-Based Algorithms in Cloud Computing: A Comprehensive Review

Hota, Arunima; Mohapatra, Subasish; Mohanty, Subhadarshini

doi:10.1007/978-981-10-8055-5_10

Cited by 35 publications

(13 citation statements)

References 17 publications

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“…ArunimaHota et al [36] conducted a review of 29 research publications on various load balancing techniques. Based on the sort of method utilized, they divided load balancing algorithms into three categories: heuristic, metaheuristic, and hybrid.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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Section: Literature Reviewmentioning

confidence: 99%

“…This includes techniques based on Tabu search, multi-agent systems, PSO, multi-population GA, Job Spanning Time and Load Balancing (JLGA), and hybridization of good features of JLGA (HJLGA). The details of these algorithms can be observed in [9,36].…”

Section: Ga and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

View full text Add to dashboard Cite

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“…Tasks that have accomplished their fulfillment inside a specified period [72] and maximum no. of the dead (or served) function each unit of time [95].…”

Section: ) Lb Performance Parameter With Qualitative Attributesmentioning

confidence: 99%

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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“…There are many more load balancing algorithms presented in the literature [17] like Artificial Bee Colony Algorithm [18], Firefly algorithm, Honey Bee algorithm, Adaptive incremental genetic algorithm [19], and also some hybrid based algorithms [20]. The critical analysis of literature review has been given in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing in Cloud Computing Using Hungarian Algorithm

Bala¹,

Chishti²

2019

IJWMT

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Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability. The user requests are fulfilled by providing a virtual machine with the requested configuration. However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour. Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem. In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS). The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters. Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS. The simulations were carried out under different workload conditions to validate our results.

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Survey of Different Load Balancing Approach-Based Algorithms in Cloud Computing: A Comprehensive Review

Cited by 35 publications

References 17 publications

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

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

Load Balancing in Cloud Computing Using Hungarian Algorithm

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