Bio inspired vm packing methods rarely cut data centre energy use
Do Bio-Inspired Algorithms Save Energy in Data Centres? Separating Server Packing from Migration Search in Virtual Machine Consolidation
Distributed, Parallel, and Cluster ComputingEmerging Technologies
Summary
Data centres use a lot of energy, so finding ways to save power is important. The authors studied special algorithms inspired by nature, like ant colonies and swarms, that try to pack virtual machines efficiently. They found that much of the energy savings come from how the virtual machines are initially placed and which servers are turned off, not from the fancy nature-inspired search methods. In many cases, these advanced algorithms actually used more energy or had no effect. This means simpler steps matter more than these complex searches for saving energy in data centres.
What this means in practice
- •For data centre operators: Use initial VM placement and host shutdown strategies to reduce energy, rather than relying on complex bio-inspired migration searches.
- •For cloud infrastructure engineers: Design VM consolidation tools that prioritize initial packing methods over metaheuristic migration optimizations to better save energy.
Authors
Yichiet Aun, Yu Hang Woo
Abstract
Energy-efficient virtual machine (VM) consolidation matters as data centre electricity demand rises. Bio-inspired metaheuristics, including ant colony optimisation (ACO), particle swarm optimisation (PSO), and genetic algorithms, have reported substantial energy savings. However, these improvements are commonly evaluated against conventional allocation policies that differ in both initial VM placement and subsequent migration optimisation, making it difficult to determine the contribution of metaheuristic search. This study reproduces an existing consolidation implementation in CloudSim Plus and introduces a matched control that retains the same initial packing and empty-host deactivation mechanisms but excludes migration search. ACO, PSO, and a modified genetic algorithm (MGA) are evaluated across two fleet configurations and four workload regimes, with ten stochastic replications per condition. The matched control achieves energy savings of 0.8-81.4% relative to the spreading baseline, the amount available falling sharply as load rises, without migrations or incomplete workload execution. Across 24 comparisons, metaheuristic search increases energy consumption in 11 cases, produces identical outcomes in seven, and reduces consumption in six. Of these, ten increases and five reductions remain statistically significant after Holm correction. Five of the six apparent reductions coincide with unfinished tasks and disappear after normalisation by delivered computational work. Only ACO under light heterogeneous load retains a 3.22-percentage-point improvement (95% CI 2.96-3.47). These findings demonstrate that initial packing and host deactivation account for most of the measured energy savings in the examined implementations, highlighting the importance of matched control conditions and workload-completion measures for reliable evaluation of VM consolidation algorithms.