Scheduling Large Task Graphs in Parallel Using a Fault-Tolerant Heterogeneous-Cluster-Based Search

—A natural approach for scheduling tasks to a workstation cluster is to employ the multiple machines in the cluster to schedule the task graphs so that the cluster manifests itself as a “self-scheduled” platform. A few parallel approaches have been devised for scheduling task graphs using a parallel machine such as an Intel Paragon but they are not suitable for a cluster of workstations environment because the machines in a cluster are heterogeneous and may experience failure. In this paper, we propose a new approach, called Cluster-Based Search (CBS), for scheduling large task graphs in parallel on a heterogeneous cluster of workstations. The CBS algorithm uses a parallel random neighborhood search which works by refining multiple different initial schedules simultaneously using different workstations. Heterogeneity of machines is handled in the biased partitioning of the search space. The parallel random neighborhood search is fault-tolerant in that the workload of a failed workstation is automatically redistributed to other workstations so that the search can continue. Our performance evaluation indicates that the CBS approach can generate high quality solutions and is scalable.