Excerpt

Introduction

As computation continues to migrate from personal computers to large-scale data centers, the energy required to run computers has become a significant economic and environmental concern. For example, between 2005 and 2010, data center electricity consumption grew by 24%. By 2014, data centers accounted for 1.8% of US energy consumption (Shehabi et al. 2016). Although this large energy footprint has led to some mitigation efforts, energy consumption in data centers continues to rise. Current estimates project that US energy use will increase a further 4% from 2014 to 2020. In 2016, in anticipation of the possible environmental impact of its growing energy demand, Google announced a $2.5 billion commitment to the purchase of energy from renewable sources.

Computer hardware efficiency directly affects data center energy consumption, and this effect is multiplied by the support systems required for deployment. Mechanical and electrical systems, such as lighting, cooling, air circulation, and uninterruptible power supplies, can quadruple the power required by the computational hardware itself (Hoelzle and Barroso 2009).

The software running in the center can further multiply energy consumption. For example, data centers must be provisioned with sufficient hardware to run the desired algorithms in a timely fashion. Algorithmic inefficiencies in software implementations can increase run times, leading to greater emphasis on parallelism to compensate. Contention for resources like networks, disks, memory, or caches leads to overprovisioning hardware (Mars et al. 2012). At sufficient scales, hardware reliability concerns require implementation of redundant resources and computations; for example, Microsoft implements redundancy for all customer data in Azure storage accounts to meet the uptime guaranteed by its service-level agreements.2 Because the load on the support systems scales with computational load, improving computational efficiency could significantly reduce overall energy costs of data centers.

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