Load Balancing in Cloud Computing using Mutation Based Particle Swarm Optimization

Agarwal, Ronak; Baghel, Neeraj; Khan, Mohd. Aamir

doi:10.1109/ic3a48958.2020.233295

Cited by 22 publications

(10 citation statements)

References 10 publications

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“…An optimized load balancing method called Mutation based on Particle Swarm Optimization (MPSO) is used in [5]. Their work is proposed to increase the efficiency of the cloud during the process of search as they used allocating resources and de-allocating of cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Cloud computing is used in demand so users should pay for services to get access easily and flexibly [5,6]. Users face many challenges and issues to deal with cloud computing technology because of workload so CC technology requires many experiments to improve and develop its usability of this technology [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Load balancing algorithms become the key factor to achieve maximum performance for the cloud environment. These algorithms have some metrics and parameters that will be discussed next in the load balancing section to cover the challenges of cloud computing [1,5,9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Hessen¹,

Abdul-Kader²,

Khedr³

et al. 2023

Computers, Materials &Amp; Continua

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According to the advances in users' service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources. Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload. A load balancing algorithm is developed to serve users' requests to improve the solution of workload problems with an efficient distribution. The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability of CC. Then, the server's availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance. Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and (low resources and large number) of IoT.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Hessen¹,

Abdul-Kader²,

Khedr³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Authors in [38] have presented a mutation-based PSO task scheduling algorithm. The fitness value of each particle is updated for every iteration.…”

Section: Related Workmentioning

confidence: 99%

PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Adil

Nabi

Raza

2022

cai

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Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI).

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“…These weights help to identify the optimal solution. In this research, the objective function has also been proposed, which is different than compared to the existing ones, and the scheduling of tasks is considered for heterogeneous virtual machines [56]. A task-based load balancing optimization method is used.…”

Section: Pso 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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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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Load Balancing in Cloud Computing using Mutation Based Particle Swarm Optimization

Cited by 22 publications

References 10 publications

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

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

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