Fereydoun Farrahi Moghaddam, Reza Farrahi Moghaddam, Mohamed Cheriet


Optimization problem of physical servers consolidation is very important for energy efficiency and cost reduction of data centers. For this type of problems, which can be considered as bin-packing problems, traditional heuristic algorithms such as Genetic Algorithm (GA) are not suitable. Therefore, other heuristic algorithms are proposed instead, such as Grouping Genetic Algorithm (GGA), which are able to preserve the group features of the problem. Although GGA have achieved good results on server consolidation in a given data center, they are weak in optimization of a network of data centers. In this paper, a new grouping genetic algorithm is introduced which is called Multi-Level Grouping Genetic Algorithm (MLGGA), and is designed for multi-level bin packing problems such as optimization of a network of data centers for carbon footprint reduction, energy efficiency, and operation cost reduction. The new MLGGA algorithm is tested on a real world problem in a simulation platform, and its results are compared with the GGA results. The comparison shows a significant increase in the performance achieved by the proposed MLGGA algorithm.


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Paper Citation

in Harvard Style

Farrahi Moghaddam F., Farrahi Moghaddam R. and Cheriet M. (2012). MULTI-LEVEL GROUPING GENETIC ALGORITHM FOR LOW CARBON VIRTUAL PRIVATE CLOUDS . In Proceedings of the 2nd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-8565-05-1, pages 315-324. DOI: 10.5220/0003903303150324

in Bibtex Style

author={Fereydoun Farrahi Moghaddam and Reza Farrahi Moghaddam and Mohamed Cheriet},
booktitle={Proceedings of the 2nd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
SN - 978-989-8565-05-1
AU - Farrahi Moghaddam F.
AU - Farrahi Moghaddam R.
AU - Cheriet M.
PY - 2012
SP - 315
EP - 324
DO - 10.5220/0003903303150324