Search Results - "Nazarova, I A"

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  1. 1

    Control model of the phased upgrade of a heterogeneous computing system by Malashenko, Yu. E., Nazarova, I. A.

    “…Upgrade options for a high-performance specialized system designed to solve resourceintensive problems, such as exhaustive searches, are considered. The…”
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    Journal Article
  2. 2

    Metric Estimates of Nodal Multiflows in a Multiuser Network System by Malashenko, Yu. E., Nazarova, I. A.

    “…In computational experiments, when transmitting flows along the shortest routes, monopoly and limit modes of management of a multiuser network are analyzed. A…”
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  3. 3

    Resource Distribution and Balancing Flows in a Multiuser Network by Malashenko, Yu. E., Nazarova, I. A.

    “…Balancing strategies for control flows and resources are studied in computational experiments on a mathematical model of a multiuser network. An algorithmic…”
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  4. 4

    Analysis of Nodal Multiflows in a Multiuser System with Equalizing Control Strategies by Malashenko, Yu. E., Nazarova, I. A.

    “…Within the framework of computational experiments on a mathematical model of a multiuser network communication system, the dynamics of changes in the…”
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  5. 5

    Quantitative Analysis of Flow Distributions in a Multiuser Telecommunication Network by Malashenko, Yu. E., Nazarova, I. A.

    “…Strategies are considered for the distribution of flows over various transmission routes in a multicommodity network model. Estimates of feasible network loads…”
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  6. 6

    Normative dynamic analysis of extreme operational modes of a heterogeneous computing system by Malashenko, Yu. E., Nazarova, I. A.

    “…A dynamic multiparameter method for analyzing the performance of a heterogeneous computing system designed for executing various resource-intensive tasks is…”
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  7. 7

    Analysis of the Load Distribution and Internodal Flows under Different Routing Strategies in a Multiuser Network by Malashenko, Yu. E., Nazarova, I. A.

    “…Within the framework of computational experiments using a multicommodity network model, the functionality of a telecommunications system facilitating…”
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  8. 8

    Estimate of Resource Distribution with the Shortest Paths in the Multiuser Network by Malashenko, Yu. E., Nazarova, I. A.

    “…Two resource distribution strategies are analyzed for the transmission of various types of flows in computational experiments on a multicommodity flow model…”
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  9. 9

    Control of resource-intensive computations under uncertainty. III. Dynamic concurrent resource allocation by Malashenko, Yu. E., Nazarova, I. A.

    “…Control problems for a heterogeneous system executing heterogeneous search tasks are considered. Data parallelization of tasks under objective uncertainty—for…”
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  10. 10

    Heterogeneous Flow Distribution at the Peak Load in the Multiuser NetworkK by Malashenko, Yu. E., Nazarova, I. A.

    “…Solutions of multicriteria optimization problems of finding nondiscriminatory distributions of tantamount, noninterchangeable flows of various types…”
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  11. 11

    Analysis of Critical Damage in the Communication Network. IV: MultiCriteria Estimations of Cluster Vulnerability by Malashenko, Yu. E., Nazarova, I. A.

    “…Changes in feasible informational flows within cluster associations are analyzed on the model of a multiuser communication system when fragments of a network…”
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  12. 12

    Analysis of Critical Damage in the Communication Network: III. Analysis of Internode Flows by Malashenko, Y. E., Nazarova, I. A.

    “…Changes in the values of the maximal permissible flows that can be transmitted between nodes in the case of network damage are analyzed through computational…”
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  13. 13

    Analysis of Equal-Share and Equalized Flow Distribution at the Peak Load in the Multiuser Network by Malashenko, Y. E., Nazarova, I. A.

    “…On the example of a multiuser communication network model, a finite-step method of distributing internodal flows that can be simultaneously transmitted between…”
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  14. 14

    Control of resource-intensive computations under uncertainty. I. Multiparametric model by Malashenko, Yu. E., Nazarova, I. A.

    “…A complex of models, algorithms and programs designed to control a high-performance heterogeneous specialized computing system that executes heterogeneous…”
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  15. 15

    Data aggregation for scheduling of resource-intensive computations under uncertainty by Malashenko, Yu. E., Nazarova, I. A.

    “…A control process for resource-intensive computations in heterogeneous specialized environment is considered. For each task, a concept of computational job…”
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  16. 16

    Analysis of Critical Damage in the Communication Network: I. Model and Computational Experiment by Malashenko, Yu. E., Nazarova, I. A.

    “…In the computational experiments on a flow model of a communication and control network, changes in the system’s functional characteristics under destructive…”
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  17. 17

    Analysis of Critical Damage in the Communication Network. II. Guaranteed Functional Performance Estimates by Malashenko, Y. E., Nazarova, I. A.

    “…In the computational experiments on a model of a multiuser communication and control network, changes in the system’s performance under destructive effects are…”
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  18. 18

    Controlling computationally intensive heterogeneous computational tasks with directive deadlines by Malashenko, Yu. E., Nazarova, I. A.

    “…A dynamic control model for processing computationally intensive tasks that allow for arbitrary data parallelization in the heterogeneous computing system is…”
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  19. 19

    Control of resource-intensive computations under uncertainty. II. Scheduling complex by Kupalov-Yaropolk, I. K., Malashenko, Yu. E., Nazarova, I. A., Ronzhin, A. F.

    “…The CORSAR model software complex designed to analyze scheduling rules for resource-intensive heterogeneous problems of search type that can be parallelized…”
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  20. 20

    Control model for heterogeneous computational tasks based on guaranteed estimates of execution times by Malashenko, Yu. E., Nazarova, I. A.

    “…A dynamic control model for processing computationally intensive tasks with unknown execution times that allow for arbitrary data parallelization in the…”
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