Impact of Shutdown Techniques for Energy-Efficient Cloud Data Centers

Abstract

Electricity consumption is a worrying concern in current large-scale systems like datacenters and supercomputers. The consumption of a computing unit is not power-proportional: when the workload is low, the consumption is still high. Shutdown techniques have been developed to adapt the number of switched-on servers to the actual workload. However, datacenter operators are reluctant to adopt such approaches because of their potential impact on reactivity and hardware failures. In this article, we evaluate the potential gain of shutdown techniques by taking into account shutdown and boot up costs in time and energy. This evaluation is made on recent server architectures. We also determine if the knowledge of future is required for saving energy with such techniques. We present simulation results exploiting real traces collected on different infrastructures under various machine configurations with several shutdown policies, with and without workload prediction.

Experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr).

This work is integrated and supported by the ELCI project, a French FSN (“Fond pour la Société Numérique") project that associates academic and industrial partners to design and provide software environment for very high performance computing.