Authors:
Fereydoun Farrahi Moghaddam
;
Reza Farrahi Moghaddam
and
Mohamed Cheriet
Affiliation:
École de Technologie Supérieure, Canada
Keyword(s):
Cloud Computing, Virtual Private Cloud, Green IT, Carbon Footprint, Genetic Algorithm, Multi-level Grouping.
Related
Ontology
Subjects/Areas/Topics:
Cloud Applications Performance and Monitoring
;
Cloud Computing
;
Cloud Computing Enabling Technology
;
Cloud Middleware Frameworks
;
e-Business
;
e-Governance
;
Energy and Economy
;
Enterprise Information Systems
;
Load Balancing in Smart Grids
;
Mobility
;
Performance Development and Management
;
Platforms and Applications
;
Smart Grids
Abstract:
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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