Intelligent Optimization-Based Energy-Efficient Networking in Cloud Services for Multimedia Big Data

We study the problem of energy-efficient networking in cloud services with geographically distributed data centers for multimedia big data applications. This is significantly challenged by the dynamic end-to-end request demands and unbalanced link energy efficiency, the unbalanced and time-varying link utilization, and the bandwidth and delay constraints for service requirements. To solve these issues, we propose a multi-constraint optimization model for energy efficiency optimization in cloud computing services where data centers are geographically distributed and are interconnected by the cloud network. Our model jointly optimizes the energy efficiency in data centers and the cloud network. More specifically, we present an intelligent heuristic algorithm to solve this model for the dynamic request demands between different data centers and between data centers and users. This is implemented by combining the niche genetic algorithm and random depth-first search. Simulation results for energy-efficient networking show that better gains in network energy efficiency can be achieved by our joint optimization. Additionally, the joint optimization between data centers and the cloud network can further improve energy savings and link utilizations for time-varying requests.