Using different scheduling algorithms can affect the performance of mobile cloud computing using Hadoop MapReduce framework. In Hadoop MapReduce framework, the default scheduling algorithm is First-InFirst-Out (FIFO). However, the FIFO scheduler simply schedules tasks according to their arrival time and does not consider any other factors that may have great impact on system performance. As a result, FIFO cannot achieve good performance in Hadoop for mobile cloud computing. In this paper, we propose a novel scheduling algorithm, called FSLA (FIFO with Shareability and Locality Aware). FSLA is a FIFObased scheduling policy that considers locality of required data and data sharing probability between tasks. The tasks requesting the same data can be gathered, easily batch processed, and thus reduce the overhead of transferring data between data nodes and computations nodes. The simulation results show that compared to FIFO, FSLA can reach 65% improvement in system performance.
