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

  D.Y. Kravchenko , Y.A. Kravchenko, V.V. Markov , A. E. Saak

  2021-11-14

  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.

• CENTRAL-RING POLYNOMIAL ALGORITHM FOR DISTRIBUTION OF COMPUTATION-TIME RESOURCES IN GRID SYSTEMS

  D.Y. Kravchenko, Y.A. Kravchenko, E.V. Kuliev, A.E. Saak

  2022-08-09

  Abstract ▼

  The article is devoted to solving the problem of computational and time resources distribution
  in grid systems based on the adaptation of polynomial algorithms to quadratic types of user applications.
  The relevance of demand distribution validity problem for the distributed computing paradigm
  in the context of information redistribution and uncertainty. The article deals with the problems of
  scheduling heterogeneous computing resources in solving complex professional and scientific problems
  achieved at different points in time, based on identifying resources by significant manifestations
  of commitment and probability. A comparative review of consumption has been carried out. The
  statement of the problem to be solved in the chosen research area is formulated. The problem of
  scheduling a grid system with a centralized multiarchitecture, which uses the task solution of a
  group-site, is substantiated. The use of this architecture requires the development of heuristic algorithms
  for the distribution of computing resources, taking into account the properties of application
  arrays and assessing the schedule compliance. Eliminating the occurrence of scheduling errors requires
  the development of a formal apparatus that will identify the prospects of the application, introduce
  their typing and build heuristic algorithms with quality assessment, selected for certain types.
  The development of such a formal apparatus is an urgent task. An equally important task within the
  framework of this mechanism is the construction of resource parity models and interaction between
  users and the computing system models. The authors proposed to solve the problem of scheduling
  computing resources based on the development and study of polynomial scheduling algorithms for
  arrays of hyperbolic applications. The main theoretical accuracy of this study is the creation of a
  formal scheduling apparatus, including the definition of resource sugar, as a model of user applications,
  based on the performance of an operation in the scheduling environment on a set of resource
  muscles. The scientific novelty of the research lies in the development of a central-ring polynomial
  algorithm for the distribution of computational time resources in grid systems, which involves an
  automatic scheduling algorithm for computing systems, adaptation to quadratic types of user applications
  and improves the efficiency of computational time resources distribution. To evaluate the
  developed efficiency of the software application algorithm and the conducted computational experiment
  with rapidly generated classes of computational resources. Obtained comparative results of the
  proposed algorithm practical efficiency experimental studies for the distribution of computational
  and time resources. The described studies have a high level of theoretical and practical significance
  and are directly related to the solution of artificial intelligence classical problems.