Periodic Linear Programming with applications to real-time scheduling

Abstract

In this paper we introduce a new mathematical modelling technique called Periodic Linear Programming; the periodic properties of Periodic Linear Programs (PLPs) permit the specification of inter-period constraints in embedded systems, in a straightforward and natural manner. We analyse PLPs in which the relationship between program variables is restricted to the class of difference constraints. Our analysis establishes that such PLPs can be reduced to simple linear programs, and hence decided in polynomial time. The class of difference constraints is extremely important from the perspective of embedded systems design, in that it permits the specification of complex timing constraints in real-time specification languages. A PLP can be thought of as a finite-description tool that represents infinite-state systems; although we use this tool purely for the purpose of modelling real-time scheduling problems, PLPs also find applications in other areas, such as concurrency design. In studying this programming paradigm, we develop novel techniques that, to the best of our knowledge, are not part of the literature. We build on the PLP structure to introduce a generalisation called Periodic Quantified Linear Programming; this programming paradigm permits the specification and analysis of uncertainty in the parameters of a PLP. Consequently, a Periodic Quantified Linear Program (PQLP) is the natural modelling tool to capture the requirements of periodic, embedded systems that are characterised by uncertainty in the execution times of processes, periodicity and relative timing constraints. In this paper, we use the PQLP structure to model and solve the periodic version of the zero-clairvoyant scheduling problem. Modelling uncertainty in the problem description is a typical technique used to incorporate a measure of fault-tolerance in the specification.