BIOINSPIRED METHOD FOR CLASSIFICATION OF DISTRIBUTED RESOURCES FOR DISPATCHING IN GRID-COMPUTING

Abstract

The article is devoted to solving the problem of scheduling distributed computing resources based on their classification by the bioinspired search method to improve the efficiency of gridcomputing functioning. The relevance of the problem is justified by a significant increase in the demand for the paradigm of distributed computing in conditions of information overflow and uncertainty. The article deals with the problems of scheduling heterogeneous computing resources when solving complex professional and scientific problems arriving at different points in time, based on the classification according to significant signs of resource compliance and readiness. A comparative review of existing analogues is carried out. The formulation of the problem to be solved in the context of the selected research topic is formulated. The strategy of choosing bioinspired modeling for solving the problem has been substantiated. The aspects of various decentralized bioinspired methods effectiveness of the use are analyzed. It is proposed to solve the problem of scheduling computational resources based on determining the correspondence of the resource to the required class. The classification is carried out on the basis of the bioinspired optimization method application, built on the basis of the Fish School Search algorithm. The use of the population bioinspired method allows us to provide unprecedented parallelism in obtaining alternative solutions and to optimize the distribution of available computing resources depending on the sets of significant features. The object of the research is the processes of data classification, which include ordered sequences of actions aimed at the distribution of computing resources by classes of problems to be solved. The subject of the research is bioinspired methods for solving the problem of data classification in grid-computing. To evaluate the effectiveness of the proposed method, a software application was developed and a computational experiment was carried outwith a different number of computing resources generated classes. Each computing resource has a certain set of attributes, which is a vector of its features. The cosine measure of the similarity between a resource attributes vector and a certain class attributes vector is a classification criterion. To improve the quality of the dispatching process, the task of classifying computing resources is solved for a variety of options for organizing the flows of complex tasks to be solved in gridcomputing. The obtained quantitative estimates demonstrate the time savings in solving the problems of scheduling distributed computing resources based on their classification by the bioinspired search method at least 7 %. The time complexity in the considered examples was . The described studies have a high level of theoretical and practical significance and are directly related to the solution of artificial intelligence classical problems.