A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Non-clairvoyant scheduling of multiple bag-of-tasks applications
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Interference-driven resource management for GPU-based heterogeneous clusters
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Self-Healing of Operational Workflow Incidents on Distributed Computing Infrastructures
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
COSMIC: middleware for high performance and reliable multiprocessing on xeon phi coprocessors
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
Multiple objective scheduling of HPC workloads through dynamic prioritization
Proceedings of the High Performance Computing Symposium
Efficient programming paradigm for video streaming processing on TILE64 platform
The Journal of Supercomputing
Self-healing of workflow activity incidents on distributed computing infrastructures
Future Generation Computer Systems
Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization
Journal of Parallel and Distributed Computing
A path priority-based task scheduling algorithm for heterogeneous distributed systems
International Journal of Communication Networks and Distributed Systems
| Hi-index | 14.98 |
Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper, we deal with the problem of scheduling multiple applications, made of collections of independent and identical tasks, on a heterogeneous master-worker platform. The applications are submitted online, which means that there is no a priori (static) knowledge of the workload distribution at the beginning of the execution. The objective is to minimize the maximum stretch, i.e., the maximum ratio between the actual time an application has spent in the system and the time this application would have spent if executed alone. On the theoretical side, we design an optimal algorithm for the offline version of the problem (when all release dates and application characteristics are known beforehand). We also introduce a heuristic for the general case of online applications. On the practical side, we have conducted extensive simulations and MPI experiments, showing that we are able to deal with very large problem instances in a few seconds. Also, the solution that we compute totally outperforms classical heuristics from the literature, thereby fully assessing the usefulness of our approach.