Multi objective optimization strategy suitable for virtual cells as a service

Abstract

Performance guarantee and management complexity are critical issues in delivering next generation infrastructure as a service (IAAS) cloud computing model. This is normally attributed to the current size of datacenters that are built to enable the cloud services. A promising approach to handle these issues is to offer IAAS from a subset of the datacenter as a, biologically inspired, virtual service cell. However, this approach requires effective strategies to ensure efficient use of datacenter resources while maintaining high performance and functionality for the service cells. We present a multi-objective and multi-constraint optimization (MOMCO) strategy based on genetic algorithm to the problem of resource placement and utilization suitable for virtual service cell model. We apply a combination of NSGA-II with various crossover strategies and population sizes to test our optimization strategy. Results obtained from our simulation experiment shows significant improvement on acceptance rate over non optimized solutions.