Power-efficient Load Distribution in Heterogeneous Computing Environments

High performance servers of heterogeneous computing environments, as can be found in data centers for cloud computing, consume immense amounts of energy even though they are usually underutilized. In times when not all computing capabilities are needed the task to be solved is how to distribute the computational load in a power-efficient manner. The question to be answered is, what load partitions should be assigned to each physical server so that all work is done with minimal energy consumption. This problem is closely related to the selection of physical servers that can be switched off completely to further reduce the power consumption. In this work, we present algorithms which calculate a power-efficient distribution of a divisible workload among multiple, heterogeneous physical servers. We assume a fully divisible load to calculate an optimized utilization of each server. Based on this distribution, an iterative process is carried out to identify servers, which can be switched off in order to further reduce the power consumption. With that information, workload (re)distribution can take place to partition appropriate subloads to the remaining servers. As before, the calculated partitioning minimizes the power consumption.