Publications

The proliferation of cloud computing due to its attracting on-demand services leads to the establishment of geo-distributed data centers with thousands of computing and storage nodes. Consequently, many challenges exist for cloud providers to run such an environment. One important challenge is to minimize cloud users’ network latency while accessing services from the data centers. The other is to decrease the data centers’ energy consumption that contributes to high operational cost rates, low profits for cloud providers, and high carbon non-environment friendly emissions. In this paper, we studied the problem of virtual machine placement that results in less energy consumption, less CO2 emission, and less access latency towards large-scale cloud providers operational cost minimization. The problem was formulated as multi-objective function and an intelligent machine-learning model constructed to improve the performance of the proposed model. To evaluate the proposed model, extensive simulation is conducted using the CloudSim simulator. The simulation results reveal the effectiveness of PCVM model compared to other competing virtual machine placement methods in terms of network latency, energy consumption, CO2 emission and operational cost minimization.