Immune-inspired technique for optimizing server's energy consumption

This paper presents an immune-inspired technique for optimizing a server energy consumption. The proposed technique is similar with an artificial immune system associated to a server, aiming to detect non-optimal server energy consumption states and to take the appropriate actions that would bring the server into an optimal state. The optimization technique has two main stages: an initialization stage and a self-optimization stage. In the initialization stage the server is monitored for a specific period of time to collect energy consumption historical raw data for identifying associations between the server energy consumption states and the appropriate optimization actions. In the self-optimization stage, energy consumption server state snapshots are taken at regular time intervals and formally represented using a biologically-inspired antigen model. The obtained antigen is then classified as self (optimal energy consumption state) or non-self (non-optimal energy consumption state) using a set of detectors obtained in the initialization stage. For non-self antigens a biologically-inspired clonal selection approach is used to determine the actions that need to be taken to bring the server in an optimal energy consumption state.