Description:
- Distributed computing
- Edge-cloud processing
- Astronomical observation processing
Abstract
USC researchers present GCNScheduler, a scheduler employing graph convolutional networks (GCNs) that can rapidly and efficiently schedule tasks of complex applications for a given objective. The technique integrates an inter-task data dependency structure with network settings into an input graph to rapidly and efficiently develop scheduling schemes. GCNScheduler performs well in large-scale settings where past scheduling heuristics fail.
Benefits
- Allows for complex task scheduling of large-scale applications
- Operates orders of magnitude faster than the HEFT and TP-HEFT algorithms
- Trains quickly and performs well
Market Application
Efficient task scheduling is essential in complex graph-based applications such as edge-cloud computing and astronomical observation processing. Successful task scheduling both improves the utilization of computing resources and reduces costs and execution time; however, the applicability of previously proposed scheduling heuristics is limited by their long computation times. Machine learning techniques offer an opportunity to improve task scheduling methods for large-scale task graphs.
Publications
Kiamari, Mehrdad, and Bhaskar Krishnamachari. "GCNScheduler: scheduling distributed computing applications using graph convolutional networks." Proceedings of the 1st International Workshop on Graph Neural Networking. 2022. https://doi.org/10.1145/3565473.3569185
Stage of Development
- Tested against HEFT and TP-HEFT algorithms
- Available for licensing