Scholarlink

<p>Multi-cloud refers to utilizing various cloud computing platforms inside one heterogeneous structure. As an NP-Hard issue, workflow management in multi-cloud technology has numerous strategies and heuristics developed. In this study, we suggest a novel framework for multi-task killer whale-driven grey wolf optimization (MKW-GWO) that optimizes multi-cloud via innovative management of workflows. The convergence speed is improved when the MKW-GWO generates arbitrary numbers because it strikes an excellent equilibrium between exploration and exploitation. After that, considerations are taken and MKW-GWO is employed for workflow management challenges in multi-cloud computing settings. This technique uses a knee-point technique for choosing a remedy from the Pareto front, which is then utilized for assigning jobs to workflows in a multi-cloud context. The outcomes are assessed and comprehensive contrasts are made for the workflow management model when compared to the proposed MKW-GWO method was higher than the existing method such as makespan using a montage of 606.21, Makespan using LIGO of 764.23, EC using a montage of 67.13, EC using LIGO of 60.13. The obtained findings demonstrated that the MKW-GWO algorithm is better than other strategies for this model.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing multi-cloud architecture by employing an innovative workflow management model

  • 期刊论文

  • 发表日期：

    2025年1月22日

查看全文
  • Imran Ali,
  • Anchal Gupta,
  • Shilpi Kulshrestha,
  • Pawan Bhambu,
  • Bharat Bhushan,
  • Sanjeev Kumar Mandal

摘要

Multi-cloud refers to utilizing various cloud computing platforms inside one heterogeneous structure. As an NP-Hard issue, workflow management in multi-cloud technology has numerous strategies and heuristics developed. In this study, we suggest a novel framework for multi-task killer whale-driven grey wolf optimization (MKW-GWO) that optimizes multi-cloud via innovative management of workflows. The convergence speed is improved when the MKW-GWO generates arbitrary numbers because it strikes an excellent equilibrium between exploration and exploitation. After that, considerations are taken and MKW-GWO is employed for workflow management challenges in multi-cloud computing settings. This technique uses a knee-point technique for choosing a remedy from the Pareto front, which is then utilized for assigning jobs to workflows in a multi-cloud context. The outcomes are assessed and comprehensive contrasts are made for the workflow management model when compared to the proposed MKW-GWO method was higher than the existing method such as makespan using a montage of 606.21, Makespan using LIGO of 764.23, EC using a montage of 67.13, EC using LIGO of 60.13. The obtained findings demonstrated that the MKW-GWO algorithm is better than other strategies for this model.