A novel workload migration scheme for heterogeneous distributed computing

Dynamically partitioning of adaptive applications and migration of excess workload from overloaded processors to underloaded processors during execution are critical techniques needed for distributed computing. Distributed systems differ from traditional parallel systems in that they consist of heterogeneous resources connected with shared networks, thereby preventing existing schemes from benefiting large-scale applications. In particular, the cost entailed by workload migration is significant when the excess workload is transferred across heterogeneous distributed platforms. This paper introduces a novel distributed data migration scheme for large-scale adaptive applications. The major contributions of the paper include: (1) a novel hierarchical data migration scheme is proposed by considering the heterogeneous and dynamic features of distributed computing environments; and (2) a linear programming algorithm is presented to effectively reduce the overhead entailed in migrating excess workload across heterogeneous distributed platforms. Experiment results show that the proposed migration scheme outperforms common-used schemes with respect to reducing the communication cost and the application execution time.