Neural Network-Based Overallocation for Improved Energy-Efficiency in Real-Time Cloud Environments

This paper introduces a dynamic resource provisioning mechanism for over allocating the capacity of Cloud data centers based on customer resource utilization patterns. The proposed mechanism reduces the impact on Real-Time constraints while improvements on the overall energy-efficiency are sought. The main idea is to exploit the resource utilization patterns of each customer for smartly under allocating resources to the requested Virtual Machines. This reduces the waste produced by frequent overestimations and increases the data center availability. Consequently, it creates the opportunity to host additional Virtual Machines in the same computing infrastructure improving its energy-efficiency. In order to mitigate the negative effect on deadlines, the proposed over allocation service implements a multiplayer Neural Network to anticipate the resource usage patterns based on historical data. Additionally, a compensation mechanism for adjusting the resource allocation in cases of unexpected higher demand is also described. The experiments contrast the proposed approach against traditional "Dynamic Resource Resizing" energy-aware mechanisms and also to our previous work that implements Low-Pass-Filter as predictor. Results demonstrate meaningful improvements in energy-efficiency while time constraints are slightly affected.