Adaptive B-Greedy (ABG): A simple yet efficient scheduling algorithm

In order to improve processor utilizations on parallel systems, adaptive scheduling with parallelism feedback was recently proposed. A-Greedy, an existing adaptive scheduler, offers provably-good job execution time and processor utilization. Unfortunately, it suffers from unstable feedback and hence unnecessary processor reallocations even when the job has constant parallelism. This problem may cause difficulties in the management of system resources. We propose a new adaptive scheduler called ABG (for Adaptive B-Greedy), which ensures both performance and stability. In a direct comparison with A-Greedy using simulated dataparallel jobs, ABG shows an average 50% reduction in wasted processor cycles and an average 20% improvement in running time. For a set of jobs, ABG also outperforms A-Greedy by 10% to 15% on average in terms of both makespan and mean response time, provided the system is not heavily loaded. Our detailed analysis shows that ABG indeed offers improved transient and steady-state behaviors in terms of control-theoretic metrics. Using trim analysis, we show that ABG provides nearly linear speedup for individual jobs and good processor utilizations. Using competitive analysis, we also show that ABG offers good makespan and mean response time bounds.