From traditional clusters to cloud systems, job scheduling is one of the most critical factors for achieving high performance in any distributed environment. In this paper, we propose an adaptive algorithm for scheduling modular non-linear parallel jobs in meteorological Cloud, which has a unique parallelism that can only be configured at the very beginning of the execution. Different from existing work, our algorithm takes into account four characteristics of the jobs at the same time, including the average execution time, the deadlines of jobs, the number of assigned resources, and the overall system loads. We demonstrate the effectiveness and efficiency of our scheduling algorithm through simulations using WRF (Weather Research and Forecasting model) that which is widely used in scientific computing. Our evaluation results show that the proposed algorithm has multiple advantages compared with previous methods, including more than 10% reduction in terms of execution time, a higher completion ratio in terms of meeting soft deadlines, and a much smaller standard deviation of the average weighted execution time. Moreover, we show that the proposed algorithm can tolerate inaccuracy in system load estimation. 
An adaptive algorithm for scheduling parallel jobs in meteorological Cloud