Knowledge elicitation in multi-agent system for distributed computing management

The effective management of scalable applications for solving large problems in a heterogeneous distributed computing environment is the non-trivial problem. Scalable applications generate competitive job flows that have be executed with the help of shared resources of the environment. The promising approach to solve this problem is to use multi-agent technologies. To this end, we develop a multi-agent system for the management of scalable applications. In contrast to known multi-agent systems, our system is based on applying a special conceptual model of the environment. It includes several components of a comprehensive knowledge about both the environment and subject domains of solved problems. We propose a new approach to an elicitation of these knowledge components through an integrated use of the conceptual modelling of distributed computing, classification of jobs and resources, and parameters adjustment for agent algorithms. With this approach, specialists in various fields of distributed computing considered as users of the environment, can apply their own knowledge at different levels of the problem solving process. This flexibility and algorithm adaption are benefits of our approach. Extensive modeling and practical experiments, with variation of important parameters of applications execution, show the efficiency of our management under developed multi-agent system for scalable applications.

I. Introduction

High-performance computing (HPC) is an important component for effective solving large problems and mathematical modeling in various subject domains. HPC is based on the parallel execution of computational processes that are characterized different degrees of an interrelationship between them.