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<p>In the rapidly evolving landscape of cloud computing, effective service composition is crucial for addressing dynamic challenges in resource provisioning and scheduling. This study introduces a novel solution to enhance cloud service composition through the utilization of a Hybrid Particle Swarm Optimization (PSO) technique. The objective is to boost cloud system performance by dynamically allocating resources in response to the ever-shifting demands of diverse applications. The literature review delves into cloud services composition using the PSO algorithm within an agent-based architecture, emphasizing the prowess of the agent-based approach in resource management. However, a notable limitation lies in the absence of a well-defined method for optimal resource combination. To overcome this constraint, the proposed Hybrid PSO algorithm is introduced, demonstrating its effectiveness in generating optimal composite services. To evaluate the proposed algorithm’s efficacy, comprehensive comparisons are conducted with established techniques such as genetic algorithms, Ant Colony Optimization (ACO), and COM2. The results unequivocally showcase the superior performance of the Hybrid PSO algorithm, validating its ability to identify optimal compositions. For instance, in terms of response time, the Hybrid PSO algorithm exhibited a 25% improvement over genetic algorithms and a 15% improvement over ACO. This comparative analysis provides valuable insights into the algorithm’s robustness and positions it as a reliable solution for addressing cloud service composition challenges. The primary aim of this study is to contribute to the ongoing advancements in cloud service composition techniques, offering a valuable tool for both cloud service providers and academics. By developing the Hybrid PSO algorithm, this work seeks to optimize resource provisioning, enhance system performance, and navigate the intricacies of constantly evolving cloud environments. The presented results bear implications for elevating the overall efficacy and efficiency of cloud-based services and applications, solidifying the Hybrid PSO algorithm as a noteworthy advancement in the realm of cloud service composition.</p>

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Hybrid PSO Algorithm for Efficient Cloud Service Composition

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Giridhar Sripathi,
  • Danish Ali Khan

摘要

In the rapidly evolving landscape of cloud computing, effective service composition is crucial for addressing dynamic challenges in resource provisioning and scheduling. This study introduces a novel solution to enhance cloud service composition through the utilization of a Hybrid Particle Swarm Optimization (PSO) technique. The objective is to boost cloud system performance by dynamically allocating resources in response to the ever-shifting demands of diverse applications. The literature review delves into cloud services composition using the PSO algorithm within an agent-based architecture, emphasizing the prowess of the agent-based approach in resource management. However, a notable limitation lies in the absence of a well-defined method for optimal resource combination. To overcome this constraint, the proposed Hybrid PSO algorithm is introduced, demonstrating its effectiveness in generating optimal composite services. To evaluate the proposed algorithm’s efficacy, comprehensive comparisons are conducted with established techniques such as genetic algorithms, Ant Colony Optimization (ACO), and COM2. The results unequivocally showcase the superior performance of the Hybrid PSO algorithm, validating its ability to identify optimal compositions. For instance, in terms of response time, the Hybrid PSO algorithm exhibited a 25% improvement over genetic algorithms and a 15% improvement over ACO. This comparative analysis provides valuable insights into the algorithm’s robustness and positions it as a reliable solution for addressing cloud service composition challenges. The primary aim of this study is to contribute to the ongoing advancements in cloud service composition techniques, offering a valuable tool for both cloud service providers and academics. By developing the Hybrid PSO algorithm, this work seeks to optimize resource provisioning, enhance system performance, and navigate the intricacies of constantly evolving cloud environments. The presented results bear implications for elevating the overall efficacy and efficiency of cloud-based services and applications, solidifying the Hybrid PSO algorithm as a noteworthy advancement in the realm of cloud service composition.