MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing
<h2>Summary</h2><p dir="ltr">This study presents <b>MHSC</b> (Meta-Heuristic Scheduling with Sensitivity Computing), a novel hybrid algorithm that optimizes energy consumption, execution time, and throughput simultaneously for cloud datacenter workflows. By co...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2025
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| الموضوعات: | |
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| الملخص: | <h2>Summary</h2><p dir="ltr">This study presents <b>MHSC</b> (Meta-Heuristic Scheduling with Sensitivity Computing), a novel hybrid algorithm that optimizes energy consumption, execution time, and throughput simultaneously for cloud datacenter workflows. By combining <b>Genetic and Bat algorithms</b>, MHSC leverages the strengths of both meta-heuristics while avoiding their limitations, allowing it to reach global optima faster and escape local traps.</p><p dir="ltr">Key innovations include <b>task clustering based on workflow inputs</b>, an <b>intelligent threshold detector using sensitivity rate</b>, and a <b>hybrid inertia function</b> for faster convergence. Experimental results show that MHSC improves energy efficiency by <b>6.4%</b> and execution time by <b>8.1%</b>, particularly as the number of nodes and task dependencies increase.</p> |
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