Adaptive energy-aware scheduling method in a meteorological cloud

Abstract

This paper focuses on the energy-aware scheduling problem of moldable non-linear parallel tasks in a meteorological cloud. Such a meteorological Cloud mainly provides computing resources for the execution of meteorological models, such as Weather Research and Forecasting model (WRF). In a meteorological Cloud, the parallelism of tasks (i.e., meteorological models) can only be configured in the beginning, and the assigned resources retained exclusively until all sub-tasks have been finished. For the scheduling of those tasks, one key challenge is how to reduce the average energy consumption while guaranteeing others requirements of such tasks. We address this challenge by considering simultaneously the deadlines of tasks, the energy consumption, the system load, and the non-linear speedup of parallel tasks when we make the scheduling decision. Specifically, we propose an adaptive energy-aware scheduling method called ASSD, that is based on the Dynamic Voltage and Frequency Scaling (DVFS) model of computing resources and the speedup of tasks under different parallelisms. We evaluate our method via simulations on a meteorological cloud. Our results show that the proposed method not only increases the number of completed tasks but also significantly reduces the average energy consumption.