A novel embedded scheduler based on Markov statistics for generating high QoS

Development of schedulers for real-time embedded systems is an emerging area of research as it addresses unsolved scheduling issues like co-operative scheduling, pre-emptive scheduling, micro-kernel based scheduling, etc. Use of statistical schedulers over deterministic schedulers offers the advantage of improved bandwidth utilization, better timeliness and resource management, higher throughput and thus higher system QoS (quality of service). We report here for the first time a novel stochastic scheduling method based on optimized Markov chains where the initial scheduled Markov transition probabilities are subsequently dynamically tuned by the central scheduler to give the best system QoS.