Characterization of Different User Behaviors for Demand Response in Data Centers

Digital technologies are becoming ubiquitous while their impact increases. A growing part of this impact happens far away from the end users, in networks or data centers, contributing to a rebound effect. A solution for a more responsible use is therefore to involve the user. As a first step in this quest, this work considers the users of a data center and characterizes their contribution to curtail the computing load for a short period of time by solely changing their job submission behavior. The contributions are: (i) an open-source plugin for the simulator Batsim to simulate users based on real data; (ii) the exploration of four types of user behaviors to curtail the load during a time window, namely delaying, degrading, reconfiguring or renouncing their job submissions. We study the impact of these behaviors on four different metrics: the energy consumed during and after the time window, the mean waiting time and the mean slowdown. We also characterize the conditions under which the involvement of users is the most beneficial.