Performance analysis of Bayesian coalition game-based energy-aware virtual machine migration in vehicular mobile cloud

To provide computing and communication services to mobile clients, vehicular mobile cloud computing has gained lot of attention in recent times. However, one of the biggest challenges for the smooth execution of these services in this environment is the intelligent usage of VMs which may be overloaded due to numerous requests from mobile clients such as vehicles and mobile devices to access these services. However, poor utilization of VMs in this environment causes a lot of energy to be wasted. To address this issue, we propose Bayesian coalition game as-aservice for intelligent context-switching of VMs to support the above defined services in order to reduce the energy consumption, so that clients can execute their services without a performance degradation. In the proposed scheme, we have used the concepts of learning automata (LA) and game theory in which LA are assumed as the players such that each player has an individual payoff based upon the energy consumption and load on the VM. Players interact with the stochastic environment for taking action such as the selection of appropriate VMs and based upon the feedback received from the environment, they update their action probability vector. The performance of the proposed scheme is evaluated by using various performance evaluation metrics such as context-switching delay, overhead generated, execution time, and energy consumption. The results obtained show that the proposed scheme performs well with respect to the aforementioned performance metrics. Specifically, using the proposed scheme there is a reduction of 10 percent in energy consumption, 12 percent in network delay, 5 percent in overhead generation, and 10 percent in execution time.