Response time analysis of dynamic load balancing algorithms in Cloud Computing

Vidya, S. Handur; Prakash, R.

doi:10.1109/worlds450073.2020.9210305

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…These factors include the availability of food and the risk of the existence of carnivores. Therefore, the birds fly synchronically unless or until they find the best place to land, and finally, they land at once [49].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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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: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The gathering of honey bees is called a swarm, which can adequately conclude the full mission by cooperation. Different researchers used the ABC algorithm for improvements in load balancing [49].…”

Section: Abc 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

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show abstract

“…Another study analyzed the response time of dynamic loadbalancing, their results showed that all algorithms used in the simulation, THR, ESCE and Particle Swarm Optimization (PSO) gave the same results when the resource is homogenous. Otherwise, entitled to swarm intelligence ability that the task can be executed more efficiently [33]. Most proposed work has proven that the existence of a variety of cloud computing aspects needs more systematic and advanced algorithms to cater for the complicated environment [34].…”

Section: Introductionmentioning

confidence: 99%

Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

Zamri¹,

Pakhrudin²,

Saaidin³

et al. 2023

IJACSA

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Cloud computing is one of the significant technologies that is used to provide seamless internet surfing for large-scale applications and data storing purposes. The cloud is described as a large platform that enables users to access data from the internet without needing to buy storing space in their equipment such as a computer. Many studies have analysed the load-balancing technique on the cloud to distribute tasks equally between servers using the Equally Spread Current Execution (ESCE) algorithm. ESCE, which is a dynamic load balancer, has quite a few problems such as average level performance and too long of response time which affected the Quality of Services. This research has simulated a cloud computing concept using the ESCE Load Modelling technique with the CloudAnalyst simulator for three servers of Data Center (DC) locations. The ESCE was simulated to enhance its algorithm's performance as a load balancer and higher throughput in the cloud environment. The result shows that ESCE average overall response time is shortest when the DC is located at R0 with response times of 15.05s, 13.05s with 10 VMs, and 8.631s with the Optimize Response Time brokerage policy. This research is significant to promote notable load-balancing technique testing for virtualized cloud machines data centers on Quality of Services (QoS) aware tasks for Internet of Things (IoT) services.

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Artificial Bee Colony Optimization-Based Load Balancing in Distributed Computing Systems—A Survey

Handur

Deshpande

2022

Lecture Notes in Networks and Systems

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Response time analysis of dynamic load balancing algorithms in Cloud Computing

Cited by 5 publications

References 10 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

Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

Artificial Bee Colony Optimization-Based Load Balancing in Distributed Computing Systems—A Survey

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