Abstract
Cloud computing continues to play a major role in transforming the IT industry by facilitating elastic on-demand provisioning of computational resources including processors, storage and networks. This is necessarily accompanied by the creation, and refreshes, of large-scale systems including cluster, grids and datacenters from which such resources are provided. These systems consume substantial amounts of energy, with associated costs, leading to signi cant CO2 emissions. In 2014, these systems consumed 70 billion kWh of energy in US; this is 1.8% of the US total energy consumption, and future consumption is expected to continue around this level with approximately 73 billion kWh by 2020. The energy bills for major cloud service providers are typically the second largest item in their budgets due to the increased number of computational resources. Energy effciency in these systems serves the providers interests in saving money to enable reinvestment, reduce supply costs and also reduces CO2 emissions. In this paper, we discuss energy consumption in large scale computing systems, such as scientfii c high performance computing systems, clusters, grids and clouds, and whether it is possible to decrease energy consumption without detrimental impact on service quality and performance. We discuss a number of approaches, reported in the literature, that claim to improve the energy efficiency of such large scale computing systems, and identify a number of open challenges. Key fi ndings include: (i) in clusters and grids, use of system level efficiency techniques might increase their energy consumption; (ii) in (virtualized) clouds, efficient scheduling and resource allocation can lead to substantially greater economies than consolidation through migration; and (iii) in clusters, switching off idle resources is more energy efficient, however in (production) clouds, performance is affected due to demand fluctuation.