Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm: A Review

Hamidi, Ahmad; Goal, Mayak Kumar; Astya, Rani

doi:10.23919/indiacom54597.2022.9763131

Cited by 1 publication

(1 citation statement)

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“…Furthermore, many of these review papers have limitations in terms of the factors used to analyze the performance parameters of load balancing. These QoS parameters include response time, migration cost, energy conservation, throughput, convergence, and scalability [40][41][42][43][44][45]. None of the existing reviews has provided an appropriate classification for classifying various load balancing strategies.…”

Section: Research Motivationmentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

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Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

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Section: Research Motivationmentioning

confidence: 99%