Planning And Scheduling For Large-scaledistributed Systems

Many applications require computing resources well beyond those available on any single system. Simulations of atomic and subatomic systems with application to material science, computations related to study of natural sciences, and computer-aided design are examples of applications that can benefit from the resource-rich environment provided by a large collection of autonomous systems interconnected by high-speed networks. To transform such a collection of systems into a user\u27s virtual machine, we have to develop new algorithms for coordination, planning, scheduling, resource discovery, and other functions that can be automated. Then we can develop societal services based upon these algorithms, which hide the complexity of the computing system for users. In this dissertation, we address the problem of planning and scheduling for large-scale distributed systems. We discuss a model of the system, analyze the need for planning, scheduling, and plan switching to cope with a dynamically changing environment, present algorithms for the three functions, report the simulation results to study the performance of the algorithms, and introduce an architecture for an intelligent large-scale distributed system

An Ordering on Subgoals for Planning

Subgoal ordering is a type of control information that has received much attention in AI planning community. In this paper we formulate precisely a subgoal ordering in the situation calculus. We show how information about this subgoal ordering can be deduced from the background action theory. We also show for both linear and nonlinear planners how knowledge about this ordering can be used in a provably correct way to avoid unnecessary backtracking