Benchmarking the Pareto Frontier of VM Placement: A Multi-Objective Evaluation of Heuristics and Metaheuristics Algorithms

Résumé

Virtual machine (VM) placement in recent cloud systems remains a challenging and new task due to the challenge of finding the best way to use resources efficiently while also ensuring service reliability. The current study offers an in-depth comparative evaluation of six VM placement algorithms—three established heuristics (Best Fit Decreasing (BFD), First Fit Decreasing (FFD), and Worst Fit (WF)), a baseline RANDOM method, and two metaheuristics (Ant Colony Optimization (ACO) and NSGA-III)—conducted through 2,400 simulation trials. We meticulously evaluate each algorithm across four critical performance metrics: placement success rate, energy consumption, execution time, and service reliability. The statistical analysis confirmed significant differences in performance across all methods using the Kruskal-Wallis and Mann-Whitney tests. Classical heuristics achieve a placement success rate (PSR) of 87.38% with BFD and FFD operating without any service violations. These methods use more energy than other approaches, which is a drawback of their good performance. NSGA-III reduces energy usage by 11% while maintaining acceptable placement performance (71.62%). The execution time (ET) can really vary, from super-fast heuristics to more time-consuming optimization methods. The results indicate that classical heuristics provide the highest reliability. No SLA violations (SLAV) were observed in the scenarios evaluated. Classical heuristics, in particular BFD and FFD, are therefore the most reliable. The study’s findings also show that cloud providers can choose the algorithms that best match their operational priorities. These priorities may include maximizing performance, minimizing energy consumption, or ensuring service stability.

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