Multiobjective scheduling of green-powered datacenters considering QoS and budget objectives

This article presents a multiobjective approach for scheduling green-powered datacenters. We consider a bag of independent deadline-constrained tasks to be executed in a datacenter partially powered by green energy where machines and be powered on/off. The problem consists in scheduling machine state, task execution, and cooling devices to follow an energy consumption profile while simultaneously minimizing the operational budget and the QoS degradation, subject to maintaining the datacenter temperature below its maximum operational threshold. We propose an Evolutionary Algorithm empowered by a Local Search for tackling this problem. Preliminary results show promising budget reductions when compared to a greedy scheduling approach.