#9 -- Sufficiency in Data Centers: Energy Aware Resource Recommendation System
Grid'5000
Energie
Eyvaz Ahmadzada, Patricia Stolf, Jean-Marc Pierson, Laurent Lefèvre
Papier ArtefactComputing providers offer flexible, scalable and heterogeneous resources. However, their usage often leads to energy waste due to bad user choices. During a job submission, users may choose a cluster more powerful than needed for workloads, or leave the choice to scheduler. In both cases, the lack of guidance can lead to unnecessary energy consumption. We study this problem by evaluating energy gains achieved by a recommendation system that assists users with more energy efficient cluster choices. We analyze historical workloads from a resource provider to extract consumption and performance patterns. Using these profiles, we recommend clusters that can run similar jobs more efficiently.
To evaluate the accuracy of recommendations, we execute evaluation workloads. Workloads are first executed on a cluster chosen by job scheduler, then submitted to the system to obtain a recommended cluster, and finally executed on the recommended cluster and all other clusters of the computing provider. Results show that the clusters recommended by the system achieve an average relative position score of 0.97, where 0 corresponds to the worst observed cluster for a workload, and 1 to the best