Integrating Job Parallelism in Real-Time Scheduling Theory

We investigate the global scheduling of sporadic, implicit deadline, real-time task systems on multiprocessor platforms. We provide a task model which integrates job parallelism. We prove that the time-complexity of the feasibility problem of these systems is linear relatively to the number of (sporadic) tasks for a fixed number of processors. We propose a scheduling algorithm theoretically optimal (i.e., preemptions and migrations neglected). Moreover, we provide an exact feasibility utilization bound. Lastly, we propose a technique to limit the number of migrations and preemptions

Review of different approaches for optimal performance of multi-processors

We reviewed the literature used for optimal performance of multi-processor, we study different approaches in this paper. They include rate monotonic, deadline monotonic, and Earliest deadline first Algorithm. These approaches are basically used for real time scheduling systems .The problem of inconsistencies occurring in these algorithms such as those tasks whose task period is less but if not executed does not matter and whenever they are scheduled under rate monotonic scheduling algorithm the time consumed by CPU in scheduling the tasks is spent unnecessarily