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• BIOINSPIRED SIMULATION METHOD FOR SCHEDULING OF PARALLEL FLOWS APPLICATIONS IN GRID-SYSTEMS

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

  2020-07-20

  Abstract ▼

  The article is devoted to solving the problem of parallel requests scheduling flows in spatially
  distributed computing systems. The relevance of the task is justified by a significant increase in
  the demand for the distributed computing paradigm in the conditions of information overflow and
  uncertainty. The article discusses the problems of scheduling user requests that require severalprocessors at the same time, which goes beyond the classical theory of schedules. The aspects of
  the efficiency of using heuristic algorithms for scheduling planar resources are analyzed. The
  reasons for their insufficiency are determined both in terms of effectiveness and empirical approaches.
  The paper proposes to solve the problem of scheduling parallel applications based on
  the integrated application of intelligent agents coalition and an event simulation model. It is proposed
  to classify incoming applications on the basis of using a modified bio-inspired optimization
  method for cuckoo search. The joint use of a coalition of intelligent agents and a bio-inspired
  method will allow for unprecedented parallelism of calculations, and the subsequent determination
  of the processing classified applications ways on the basis of a simulation model will allow us
  to form sets of alternative solutions to speed up problem solving and optimize the distribution of
  available computing resources depending on the sets of incoming applications. To evaluate the
  effectiveness of the proposed approach, a software product was developed and experiments were
  conducted with a different number of incoming applications. Each incoming application has a
  certain set of attributes, which is a vector of the application characteristics. The degree of the
  application similarity feature vector and the vertex reference feature vector in the distributing
  simulation model is a classification criterion for the application. To improve the quality of the dispatch
  process, new procedures for duplicating unclassified applications have been introduced, which
  allow intensifying the search for matches in feature vectors. It also provides backup dispatching trajectories
  necessary for processing precedents for the appearance of applications with absolute priority
  at the inputs. The quantitative estimates obtained demonstrate time savings in solving problems of
  relatively large dimension (from 500,000 vertices) of at least 10%. The time complexity in the considered
  examples was O (n 2). The described studies have a high level of theoretical and practical significance
  and are directly related to the solution of classical problems of artificial intelligence
  aimed at finding hidden dependencies and patterns on a large set of big data.

• HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT

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

  2020-07-20

  Abstract ▼

  The article is devoted to solving the problem of mapping ontological models in the processes
  of extracting and knowledge management. The relevance and significance of this task are due to
  the need to maintain reliability and eliminate redundancy of knowledge during the integration
  (unification) of various origins structured information sources. The proximity and consistency of
  the conceptual semantics of the combined resource during the mapping is the main criterion for
  the effectiveness of the proposed solutions. The article considers the problems of choosing appropriate
  solution approaches that preserve semantics when displaying concepts. The strategy of
  choosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralized
  bio-inspired methods are analyzed. The reasons for the need for hybridization are identified.
  The paper proposes to solve the problem of mapping ontological models using a bio-inspired
  algorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms.
  The hybridization of these algorithms allowed us to combine their main advantages: a consistent
  bacterial search that provides a detailed study of local areas, and a significant number of
  the cuckoo agent during the implementation global movements of Levy flights. To evaluate the
  effectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed and
  experiments were performed on the mapping of different sizes ontologies. Each concept of any
  ontology has a certain set of attributes, which is a semantic vector of attributes. The degree of the
  semantic vectors similarity for the compared concepts of displayed ontologies is a criterion for
  their integration. To improve the quality of the display process, a new encoding of solutions has
  been introduced. The quantitative estimates obtained demonstrate time savings in solving problems
  of relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The time
  complexity of the developed hybrid algorithm is O (n 2). The described studies have a high level of
  theoretical and practical significance and are directly related to the solution of classical problems
  of artificial intelligence aimed at finding hidden dependencies and patterns on a multitude of
  knowledge elements.