Reducing the energy consumption of large-scale computing systems through combined shutdown policies with multiple constraints. (January 2018)

• Record Type: Journal Article
• Authors: Benoit, Anne; Lefèvre, Laurent; Orgerie, Anne-Cécile; Raïs, Issam
• Other Names: Dongarra Jack guest-editor.; Tourancheau Bernard guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 32:Number 1(2018)
• Page Start: 176
• Page End: 188
• Publication Date: 2018-01
• Subjects: Large-scale distributed systems -- energy models -- shutdown policies -- simulation; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342017714530
• Languages: English
• ISSNs: 1094-3420

Abstract

Large-scale distributed systems (high-performance computing centers, networks, data centers) are expected to consume huge amounts of energy. In order to address this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures: the time and energy cost of switching on and off, the power and energy consumption bounds caused by the electricity grid or the cooling system, and the availability of renewable energy. In this article, we propose models translating these various constraints into different shutdown policies that can be combined for a multiconstraint purpose. Our models and their combinations are validated through simulations on a real workload trace.