Load Balancing in Cloud Computing Using Genetic Algorithm and Fuzzy Logic

Saadat, Ali; Masehian, Ellips

doi:10.1109/csci49370.2019.00268

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…The challenges associated with load balancing, like heterogeneous endpoints, geographically distributed servers, single point of failure, virtualized migrations, management of storage and retrieval, capacity scaling, algorithms' performance, and so on, can be addressed in the future by utilizing some cutting-edge load balancing techniques; specifically, when combined with new QoS metrics and algorithm complex evaluation aspects [50]. Because cloud computing is grappling with increasing data, metaheuristic or swarm task scheduling techniques must advance in domains [51,52] such as machine learning, artificial intelligence, IoT and blockchain. Fault control, prevention, and workload transfer characteristics, which receive little emphasis in modern loadbalancing techniques, are crucial for future studies and development.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Syed,

Shaikh,

Alshahrani

et al. 2024

IEEE Access

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In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user's demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology.

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Section: Discussionmentioning

confidence: 99%

“…Saadat and Masehian [51] presented a hybridized bimodular technique for load balancing in the cloud using a genetic algorithm. The proposed methodology has proven superior to previous techniques regarding load balancing and resource utilization.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Syed,

Shaikh,

Alshahrani

et al. 2024

IEEE Access

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“…It ultimately leads to a higher user retention rate. Saadat and Masehian [27] proposed employing multi-agent GA to facilitate effective load balancing. The proposed method considers the users' priorities and, at the same time, also targets the completion time of the first task.…”

Section: Related Workmentioning

confidence: 99%

“…SI can be applied in different areas that deal with routing and specialized task scheduling methods. These two SI applications eventually led to the most discussed topic in cloud computing: ''load balancing'' SI makes load balancing simply by relying on the animals for motivation [17], [18]. Consequently, this collaboration can be used to manage load in the cloud more effectively.…”

Section: Introductionmentioning

confidence: 99%

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

et al. 2023

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Cloud Computing is the dynamic provisioning of resources to provide services to end-users over the internet. The realization of cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, load balancing is the most challenging one. Therefore, in the past few years, research into various static and dynamic algorithms to achieve optimal results is gaining importance. This research proposes Swarm Intelligence (SI) as a loadbalancing solution for cloud computing. Several alternatives in the literature (like genetic algorithm, ACO, PSO, BAT, GWO, and many others) are investigated, but none consider the load balancing convergence time with global optimization. Among these algorithms, this research emphasizes Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). A combined approach of GWO-PSO that capitalizes on the benefits of fast convergence and global optimization is proposed in this paper. These two techniques enhance system efficiency and resource allocation, working together to solve the load-balancing challenge. Compared to other traditional approaches, the findings of this research are promising while achieving globally optimized fast convergence and reducing overall response time. On average, the overall response time of the proposed technique is reduced to 12% as compared to other algorithms. Furthermore, the best optimal value obtained from the objective function of the proposed GWO-PSO algorithm improves PSO to 97.253% in terms of convergence.

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“…Hybrid Fuzzy-Genetic Algorithm: A hybrid technique has been presented to intelligent person load balancing in cloud computing [46]. Ali Saadat and Ellips Masehian proposed two modules to achieve load balancing.…”

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

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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 Genetic Algorithm and Fuzzy Logic

Cited by 15 publications

References 14 publications

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

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

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