Scheduling partially ordered tasks with probabilistic execution times
The objective of this paper is to relate models of multi-tasking in which task times are known or known to be equal to models in which task times are unknown. We study bounds on completion times and the applicability of optimal deterministic schedules to probabilistic models. Level algorithms are shown to be optimal for forest precedence graphs in which task times are independent and identically distributed exponential or Erlang random variables. A time sharing system simulation shows that multi-tasking could reduce response times and that response time is insensitive to multi-tasking scheduling disciplines.
© 1975 ACM. This research was supported by NSF Grant DCR74-13302.