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Energy-Aware Ant Colony Based Workload Placement in Clouds

Eugen Feller 1 Louis Rilling 2 Christine Morin 1 
1 MYRIADS - Design and Implementation of Autonomous Distributed Systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : With cloud computing becoming ubiquitous, cloud providers are starting to deploy increasing numbers of energy hungry data centers. Energy conservation then becomes essential, in order to decrease operation costs and increase the system reliability. One traditional approach to conserve energy in these environments is to perform workload (i.e., VM) consolidation. Thereby, workload is packed on the least number of physical machines in order to increase the resource utilization and thus be able to transition parts of the resources into a lower power state. However, most of the workload consolidation approaches applied until now are limited to a single resource (e.g., CPU) and rely on relatively simple greedy algorithms such as First-Fit Decreasing (FFD), which perform resource-dissipative workload placement. In this work, we model the workload placement problem as an instance of the multi-dimensional bin-packing (MDBP) problem and design a novel, nature-inspired algorithm based on the Ant Colony Optimization (ACO) meta-heuristic to compute the placement dynamically, according to the current load. We evaluate the ACO-based approach by comparing it with one frequently applied greedy algorithm (i.e., FFD). Our simulation results demonstrate that ACO outperforms the evaluated greedy approach as it achieves superior energy gains through better server utilization and requires less machines.
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Reports (Research report)
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Contributor : Eugen Feller Connect in order to contact the contributor
Submitted on : Monday, May 23, 2011 - 10:10:30 AM
Last modification on : Thursday, October 27, 2022 - 3:45:00 AM
Long-term archiving on: : Friday, November 9, 2012 - 11:56:09 AM


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  • HAL Id : inria-00594992, version 1


Eugen Feller, Louis Rilling, Christine Morin. Energy-Aware Ant Colony Based Workload Placement in Clouds. [Research Report] RR-7622, INRIA. 2011. ⟨inria-00594992⟩



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