Leinberger, WilliamKumar, VipinKarypis, George2020-09-022020-09-021999-05-25https://hdl.handle.net/11299/215381Recent parallel processing systems, such as the SUN E10000, are made up of pools of independently allocatable hardware and software resources such a shared memory, large disk farms, distinct I/O channels, and software licenses. In order to make efficient use of all the available system resources, the scheduling algorithm must be able to maintain a job working set which fully utilizes all of the resources. Previous work in scheduling multiple resources focused on coordinating the allocation of CPUs and memory, using ad-hoc methods for generating good schedules. We provide new job selection heuristics based on "resource balancing" which support the construction of generalized K-resource scheduling algorithms. We show through simulation that performance gains of up to 50% in average response time are achievable over classical scheduling methods such as First-Come-First-Served with First-Fit Backfilling.en-USReport