Optimization of Power Consumption in Cloud Data Centers Using Green Networking Techniques

In this paper, a neuro-based predictor is proposed with a prediction algorithm to estimate the required number of active servers simulating the Green Networking objectives. The inputs of such predictor are the CPU utilization of the servers in the data center and the variations of the incoming demands with the number of users’ variation. During the work, different demand profiles of ClarkNet traffic traces are simulated on OPNET14.5 Modeler to obtain the required training values of servers’ CPU utilization and clients’ throughput. Also, Green Networking objectives are defined to maintain the Power Management Criteria (PMC) which guaranteed that all CPU utilization must be greater than 30%. Taking into account that a maximum number of 100 servers are used in such local data center, an ON/OFF control algorithm is then suggested for the power management of different servers in data center to fulfill the previous Green objectives. The Power saving is finally evaluated since it has been noticed that the power saving percentage can be increased from 17.33% to 85.33% of a total power of 75 k watts when the number of the operating servers is decreased from 80% to 5% of the overall servers.