Decentralized Scheduling and Load Balancing for Parallel Programs
Gary Jackson, Peter J. Keleher, Alan Sussman · 2014
We present a completely decentralized algorithm for parallel job scheduling and load balancing in distributed peer-to-peer environments. This algorithm is useful for meta-scheduling across known clusters and scheduling on desktop grids. To accomplish this, we build on previous work to route jobs to appropriate resources then use the new algorithm to start parallel jobs and balance load across the grid. We also discuss what constitutes useful clustering's for this algorithm as well as inherent scaling limitations. Ultimately, we show that our algorithm performs comparably to one using centralized load balancing with global up-to-date information. The principal contribution of this work is that the parallel job scheduling is completely decentralized, which is not featured in previous work, and enables reliable ad hoc sharing of distributed resources to run parallel computations.