GreenHadoop: Leveraging Green Energy in Data-Processing Frameworks

摘要

Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) en-ergy drawn from the electrical grid, they are not always available. This means that energy demand and supply must be matched, if we are to take full advantage of the green en-ergy to minimize brown energy consumption. In this paper, we investigate how to manage a datacenter's computational workload to match the green energy supply. In particular, we consider data-processing frameworks, in which many back-ground computations can be delayed by a bounded amount of time. We propose GreenHadoop, a MapReduce frame-work for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenHadoop predicts the amount of solar energy that will be available in the near future, and schedules the MapReduce jobs to maximize the green energy consumption within the jobs' time bounds. If brown energy must be used to avoid time bound violations. GreenHadoop selects times when brown energy is cheap, while also managing the cost of peak brown power con-sumption. Our experimental results demonstrate that Green-Hadoop can significantly increase green energy consump-tion and decrease electricity cost, compared to Hadoop.