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<p>Scheduling tasks in multi-cloud settings is complex since different cloud service providers have different latency and cost limits, which must be balanced. In a multi-cloud setting, ineffective scheduling results in more significant execution costs, more latency, missed deadlines, underutilization of resources, and worse service performance. Existing approaches frequently lack effective mechanisms for balancing cost and delay, are unable to adjust dynamically to changes in workload, and ineffectively utilize resource heterogeneity, which results in less-than-ideal task distribution and higher operating expenses. By applying the concepts of quantum computing, it enhances the Gannet Optimization Algorithm (GOA) and speeds up convergence. To further enhance the global optimum solution, QIGTLC integrates Termite Life Cycle Optimizer (TLCO) with quantum-inspired GOA. The suggested approach first completes activities on the most economical and significant cloud providers while making sure that the latency rate, energy usage, and makespan are minimized. The remaining jobs that cannot be planned within the allotted resources are dynamically split among several cloud platforms according to performance, cost, and availability factors. Lastly, the suggested task scheduling strategy maximizes overall resource efficiency and minimizes execution costs by distributing workloads among several cloud providers. Experiments are carried out on NASA and HPC2N, two real-world workloads, and the outcomes are contrasted with other existing methods. The outcomes show that the suggested strategy performs better in terms of cost, energy usage, latency, deadline violation cost, and makespan, demonstrating its efficacy in multi-cloud settings.</p>

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QUANTUM Based Latency and Cost Aware Task Scheduling in a Multi-Cloud Environment

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Rakesh Reddy Gurrala,
  • Sampath Kumar Tallapally

摘要

Scheduling tasks in multi-cloud settings is complex since different cloud service providers have different latency and cost limits, which must be balanced. In a multi-cloud setting, ineffective scheduling results in more significant execution costs, more latency, missed deadlines, underutilization of resources, and worse service performance. Existing approaches frequently lack effective mechanisms for balancing cost and delay, are unable to adjust dynamically to changes in workload, and ineffectively utilize resource heterogeneity, which results in less-than-ideal task distribution and higher operating expenses. By applying the concepts of quantum computing, it enhances the Gannet Optimization Algorithm (GOA) and speeds up convergence. To further enhance the global optimum solution, QIGTLC integrates Termite Life Cycle Optimizer (TLCO) with quantum-inspired GOA. The suggested approach first completes activities on the most economical and significant cloud providers while making sure that the latency rate, energy usage, and makespan are minimized. The remaining jobs that cannot be planned within the allotted resources are dynamically split among several cloud platforms according to performance, cost, and availability factors. Lastly, the suggested task scheduling strategy maximizes overall resource efficiency and minimizes execution costs by distributing workloads among several cloud providers. Experiments are carried out on NASA and HPC2N, two real-world workloads, and the outcomes are contrasted with other existing methods. The outcomes show that the suggested strategy performs better in terms of cost, energy usage, latency, deadline violation cost, and makespan, demonstrating its efficacy in multi-cloud settings.