Task allocation in distributed computer systems through an AI planner solver

Abstract

Many avionics systems use specialized parallel architectures to speed processing and to increase system reliability. The software used therein is frequently divided into tasks and executed concurrently on multiple processors under strict real-time constraints critical to the mission's successful performance. Scheduling and planning are needed for effectively managing the computational resources on such avionics architectures. Since most real-time scheduling problems are known to be NP-hard, an approximation approach that applies heuristic methods using conventional computer algorithms has been used to solve these scheduling problems. Artificial intelligence (AI) planners have been used extensively in manufacturing scheduling and operations research. In this paper, we demonstrate the idea of using AI planners to perform scheduling through an example. We derive a solution to scheduling several image tasks on a distributed computer system, using the AI planner PRODIGY. The basic characteristics of AI planners in general and the PRODIGY solver in particular are described, the domain theory and problem specification for our problem through the PRODIGY description language PDL are presented.