Soft real-time environments consist of jobs that must receive service within a particular time interval. If service for a specific job is not completed by the end of its time interval, it is said to be lost; in addition, the computation time expended on the job is wasted, and any further computation for the job is discontinued. The goal of a system designer is to provide an environment that minimizes the number of jobs that are lost. If a parallel environment is available, the system designer has two options: Allow each processor to execute a job individually, or let multiple processors cooperate in executing a job. This article shows, for two classes of static allocation policies, that simple comparative analytical models may be used to indicate which option minimizes the number of lost jobs, as a function of workload intensity. The first class of policies, called equal partitions, statically decomposes the system into equal-size sets of processors and executes one job per partition. These policies are frequently employed in other contexts. The second class of policies, called two partitions, statically partitions the processors into two sets, not necessarily of the same size. Surprisingly, it is observed mathematically that even for statistically identical jobs, this class of policies is superior to equal partitions under certain loadings. The analysis is validated experimentally with a workload executed on a 16-node iPSC/2 hypercube.
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