Shubham Agrawal
ABSTRACT
The advent of virtualization technologies encourages
organizations to undertake server consolidation exercises for improving the overall server utilization and for minimizing the capacity redundancy within data-centers. Identifying complimentary workload patterns is a key to the success of server consolidation exercises and for enabling multi-tenancy within data-centers. Existing works either do not consider incompatibility constraints or performs poorly on the disjointed conflict graphs. The algorithm proposed in the current work overcomes the limitations posed by the existing solutions. The current work models the server consolidation problem as a vector packing problem with conflicts (VPC) and tries to minimize the number of servers used for hosting applications within datacenters and maximizes the packing efficiency of the servers utilized. This paper solves the problem using techniques inspired from grouping genetic algorithm (GGA) - a variant of the traditional Genetic Algorithm (GA). The algorithm is tested over varying scenarios which show encouraging results.
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Index Terms
- Grouping genetic algorithm for solving the serverconsolidation problem with conflicts
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