Reducing scheduling overheads in multi-processors real-time systems
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In real-time systems, it is required to complete all work on a timely basis. There are mainly two types of real time systems: hard real-time systems (HRT) and soft-real time (SRT) systems. In hard real-time systems, a missed deadline is considered a system failure; in soft real-time systems some deadlines may be missed. The aim of real-time scheduling analysis is to ensure a sequence of jobs meets their deadlines. Many real-time systems allow jobs to interrupt, or preempt, one another. In multiprocessor systems a preemption may result in a job migrating from one processor to another. Both preemptions and migrations cause scheduling overheads. In this dissertation, we present two approaches for reducing scheduling overheads. One approach reduces the number of preemptions and migrations by adjusting job priorities. Another approach incorporates genetic algorithms to classify HRT task sets, and uses heuristics to reduce the number of preemptions and migrations. Another type of overhead that this dissertation addresses is energy consumption. This dissertation presents an algorithm to use Dynamic Voltage and Frequency Scaling (DVFS) processors for conserving energy. The proposed algorithm drastically reduces the power consumption of the systems by slowing down the processors as much as possible.