Abstract
This work presents a multi-objective approach for scheduling energy consumption in data centers considering traditional and green energy data sources. This problem is addressed as a whole by simultaneously scheduling the state of the servers and the cooling devices, and by scheduling the workload of the data center, which is comprised of a set of independent tasks with due dates. Its goal is to simultaneously minimize the energy consumption budget of the data center, the energy consumption deviation from a reference profile, and the amount of tasks whose due dates are violated. Two multi-objective evolutionary algorithms hybridized with a greedy heuristic are proposed and are enhanced by applying simulated annealing for post hoc optimization. Experimental results show that these methods are able to reduce energy consumption budget by about 60% while adequately following a power consumption profile and providing a high quality of service. These results confirm the effectiveness of the proposed algorithmic approach and the usefulness of green energy sources for data center infrastructures.
Highlights
Energy consumption in data centers has become a critical matter, especially to large providers likeGoogle, Facebook, and Amazon among others
Three output variables are defined for the model: (i) the amount of overdue time required for task completion (Qt ); (ii) the internal temperature variable (Tt ) which is the temperature inside the data center; and (iii) component at time t (Ct) which is the energy required by the HVAC component, and It which is the energy required by the Computing Infrastructure (CI) component
Budget reduction is much lower for the nighttime green power profile (g3 ), with an average reduction of 46%, which accounts for just the server state and cooling device planning
Summary
Energy consumption in data centers has become a critical matter, especially to large providers like. This work presents a multi-objective problem formulation for optimizing the energy consumption of data centers that are powered by hybrid energy. The problem formulation addresses the scheduling of computational load and cooling devices in a data center in order to minimize its energy consumption budget, minimize the deviation of its energy consumption from a reference consumption profile, and maximize the Quality of the Service (QoS) provided to its users. Experimental results show the proposed approach reduces energy consumption budget by about 60% while maintaining QoS over 95% and a deviation from a reference power profile of about 3%, all this compared to a traditional business as the usual data center scenario.
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