Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Prity, Farida Siddiqi; Uddin, K. M. Aslam; Nath, Nishu

doi:10.1007/s42044-023-00163-8

Cited by 2 publications

(1 citation statement)

References 157 publications

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“…There are multiple motivations for attempting to write a research paper on the performance evaluation of various metaheuristic algorithms for load balancing in a cloudbased architecture, and the primary motivations are as follows [4][5][6][7]:…”

Section: A Motivationmentioning

confidence: 99%

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: A Motivationmentioning

confidence: 99%

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Syed,

Shaikh,

Alshahrani

et al. 2024

IEEE Access

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Improved Grey Wolf Algorithm: A Method for UAV Path Planning

Zhou,

Shi,

Zhang

2024

Drones

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The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.

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Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Cited by 2 publications

References 157 publications

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Improved Grey Wolf Algorithm: A Method for UAV Path Planning

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