The execution of a complex task in any environment requires planning. Planning is the process of constructing an activity graph given by the current state of the system, a goal state, and a set of activities. If we wish to execute a complex computing task in a heterogeneous computing environment with autonomous resource providers, we should be able to adapt to changes in the environment. A possible solution is to construct a family of activity graphs beforehand and investigate the means of switching from one member of the family to another when the execution of one activity graph fails. In this paper, we study the conditions when plan switching is feasible. Then we introduce an approach for plan switching and report the simulation results of this approach

A computational grid is a complex system. The state space of a complex system is very large and it is infeasible to create a rigid infrastructure implementing optimal policies and strategies which take into account the current state of the system. An alternative to a rigid infrastructure is to base the system's reactions on logical inference, planning, and learning, the quintessential elements of an intelligent system. An intelligent grid is one where societal services exhibit intelligent behavior. A coordination service acting as a proxy on behalf of end users reacts to unforeseen events, plans how to carry out complex tasks, and learns from the history of the system. Various policies implemented by the societal services of an intelligent grid, such as brokerage and matchmaking, are based upon rules and facts gathered with the aid of a monitoring service. The question we address is how to construct intelligent computational grids which are truly scalable and could respond to the needs of a diverse user community. We present a prototype of a system used for a virtual laboratory in computational biology.

Summary form only given. We discuss the semantics of process and case description necessary for the workflow management on a grid used for scientific applications and outline the structure of basic ontologies supporting the coordinated execution of complex tasks. We survey the core services provided by our environment and discuss in some detail the planning service.

We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.