Power Consumption Optimization in Datacenters Using PSO Tuning in Fuzzy Rule-Based Systems.

This paper presents a strategy for reducing power consumption in a data centers in cloud computing. A more efficient use of resources using optimal scheduling of tasks is proposed. The scheduling strategy uses a fuzzy rule-based system (FRBS) with automatic learning for knowledge adquisition. The learning strategy is inspired on Particle Swarm Optimization algorithm and it allows the tuning of fuzzy sets of the FRBS without the need for obtaining new rules in a way that the initial rule base introduced by an expert is maintained through the whole performance of the scheduler.