Journal of Embedded Computing

This paper studies static-priority preemptive scheduling on a multiprocessor using partitioned scheduling. We propose a new scheduling algorithm and prove that if the proposed algorithm is used and if less than 50% of the capacity is requested then all deadlines are met. It is known that for every static-priority multiprocessor scheduling algorithm, there is a task set that misses a deadline although the requested capacity is arbitrary close to 50%.

Fixed-task-priority (FTP) scheduling and earliest-deadline-first (EDF) scheduling policies are alike in fixing the priority of each job of a task at the time the job is released. This common feature of FTP and EDF scheduling permits a unified analysis of scheduling failures, to derive new sufficient tests for meeting all deadlines of a set of independent sporadic tasks under a global preemptive FTP or EDF scheduling policy. The performance of the new tests has been evaluated in comparison to prior schedulability tests, by simulation. The new tests are able to verify some schedulable task systems that could not be verified by prior tests, but also fail to verify some systems that can be verified by prior analysis techniques. The biggest gain appears to be for fixed-task-priority scheduling, especially with post-period deadlines.

We consider the problem of task reweighting in fair-scheduled multiprocessor systems wherein each task's processor share is specified as a weight. When a task is reweighted, a new weight is computed for it, which is then used in future scheduling. Task reweighting can be used as a means for consuming (or making available) spare processing capacity. The responsiveness of a reweighting scheme can be assessed by comparing its allocations to those of an ideal scheduler that can reweight tasks instantaneously. A reweighting scheme is fine-grained if any additional per-task “error” (in comparison to an ideal allocation) caused by a reweighting event is constant. In prior work on uniprocessor\/ notions of fairness, a number of fine-grained reweighting schemes were proposed. However, in the multiprocessor case, prior work has failed to produce such a scheme. In this paper, we remedy this shortcoming by presenting a multiprocessor reweighting scheme that is fine-grained. We also present an experimental evaluation of this scheme that shows that it is often much more responsive than prior (non-fine-grained) schemes in enacting weight-change requests.