A Novel Embedded Statistical Scheduler for Realising High System-QoS

Abstract

ABSTRACT-Development of schedulers for real-time embedded systems is an emerging area of research as it addresses timely scheduling issues like co-operative scheduling KEYWORDS-QoS, Schedulers, Embedded system, Markov proces , Probability of Error INTRODUTIONOptimizing the CPU usage times allocated to processes for providing high QoS is a key issue and challenge in modern embedded system design. In a multi-tasking environment where an embedded processor runs several processes, the CPU usage time provisioned for each process is analytically intractable and needs simulation study. In our simulation we consider Markov statistics to get the initial estimates for the scheduled times. They are later dynamically tuned by the scheduler using feedback control for maintaining minimum aggregate probability of error of the system and thus best system quality of service (QoS). RELATED WORKCazorla et al [1] have studied QoS for embedded systems. But the QoS considered is specific for an individual process. Peha et al [2] suggested some new approaches towards scheduling but they are not QoS-aware. Matschulat et al [3] and Tomoyoshi et al [4] have studied QoS [5][6][7] in embedded systems but none of them considered Markov chains. The concept of system QoS based on Markov statistics, is for the first time we have worked out here to our best knowledge.Most of the earlier work [3-6] on finite state modelling of schedulers considered the states of each process as of four types: 1) sleeping 2) ready 3) executing and 4) blocked. This is not an enabling technique to calculate system QoS. We, on the other hand, model here each of the processes in a multi-tasking environment as an individual Markov process and calculate the system QoS. To our best of knowledge, this is the first such approach [8,9]. METHOLODGYThis paper reports a Finite-state machine (FSM) based Markov chain model for schedulers where each process is modelled as a Markov state. The processes settle to a steady state