Parallel Application Scheduling on Networks of Workstations

Parallel applications can be executed using the idle computing capacity of workstation clusters. However, it remains unclear how to most effectively schedule the processors among different applications. Processor scheduling algorithms that were successful for shared-memory machines have proven to be inadequate for distributed memory environments due to the high costs of remote memory accesses and redistributing data. We investigate how knowledge of system load and application characteristics can be used in scheduling decisions. We propose the new algorithm AEP(2) which, by properly exploiting both the information types above, performs better than other non-preemptive scheduling rules, and nearly as well as idealized versions of preemptive rules (with free preemption). We conclude that AEP(2) is suitable for use in scheduling parallel applications on networks of workstations.