This paper considers a decentralised hybrid algorithm for scheduling scientific workflow applications onto an underlying distributed computing environment with heterogeneous resources for minimum end-to-end delay (EED). Distributed scientific workflow applications modelled as directed acyclic graphs (DAGs) are widely applied to various research areas to enable efficient knowledge discovery by automated data processing. Owing to the NP-hardness of this problem, heuristic algorithms are commonly proposed to achieve the EED. Our algorithm combines iterative critical path search and layer-based priority techniques (HICPP) to achieve the minimum EED. Four representative mapping and scheduling algorithms for minimum EED are compared with HICPP. Our simulation results illustrate that HICPP consistently achieves the smallest EED with a low algorithm running time observed from many different scales of simulated test cases.
|Number of pages||13|
|Journal||International Journal of High Performance Computing and Networking|
|State||Published - 1 Jan 2015|
- Directed acyclic graph
- Distributed computing
- Minimum end-to-end delay
- Workflow mapping and scheduling