Simulation modeling in heterogeneous distributed computing environments to support decisions making in warehouse logistics

Bychkov, I. V.; Oparin, Gennady A.; Tchernykh, Andrei; Feoktistov, Alexander; Bogdanova, Vera; Dyadkin, Yu.; Andrukhova, V.Ya.; Basharina, Olga

doi:10.1016/j.proeng.2017.09.647

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…To solve this problem, a distributed applied software package has been developed using the Orlando Tools framework [17]. This package is a parameter sweep application [18]. Simulations models (modules) are created using a special toolkit [19].…”

Section: Examplementioning

confidence: 99%

Predicting runtime of computational jobs in distributed computing environment

Feoktistov¹,

Basharina²

2020

The International Workshop on Information, Computation, and Control Systems for Distributed Environments

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The paper addresses a relevant problem of predicting the runtime of jobs for executing problem-solving schemes of large-scale applications in a heterogeneous distributed computing environment. Such an environment includes nodes that have various hardware architectures, different system software, and diverse computational possibilities. We believe that increasing the accuracy in predicting the runtime of jobs can significantly improve the efficiency of problem-solving and rational use of resources in the heterogeneous environment. To this end, we propose new models that make it possible to take into account various estimations of the module runtime for all modules included in the problem-solving scheme. These models were developed using the special computational model of distributed applied software packages (large-scale scientific applications). In addition, we compare the prediction results (jobs runtime and their errors) using different estimations. Among them are the estimations obtained through the modules testing, users estimations, and estimations based on computational history. These results were obtained in continuous integration, delivery, and deployment of applied and system software of a package for solving warehouse logistics problems. They show that the largest accuracy is achieved by the modules testing.

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Section: Examplementioning

confidence: 99%

Predicting runtime of computational jobs in distributed computing environment

Feoktistov¹,

Basharina²

2020

The International Workshop on Information, Computation, and Control Systems for Distributed Environments

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“…Thus, we can conclude that computational experiments showed the high scalability of parameter sweep computations on heterogeneous resources. Additional aspects of solving warehouse logistics problems in the heterogeneous distributed computing environment that are related to the multi-agent management of distributed computing and multi-criteria optimization of the modeling results are discussed in detail in [19,20].…”

Section: Computational Experimentsmentioning

confidence: 99%

Toolkit for simulation modeling of queue systems in Grid

Feoktistov¹,

Kostromin²,

Fereferov³

et al. 2019

The International Workshop on Information, Computation, and Control Systems for Distributed Environments 2019

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The paper addresses an important problem related to automation of simulation modeling of queue systems using high-performance computing. There is a large spectrum of tools for the simulation modeling of such systems. However, these tools often do not use the possibilities of subject-oriented high-performance computing in full. The purpose of the study is the development of the new tools for automation of the simulation modeling of queue systems in a heterogeneous distributed computing environment. To this end, we develop specialized applications for parameter sweep computations in such an environment. A methodology of the application development is based on the integrated use of methods of the conceptual and wireframe programming, methods of the computations planning and resources allocation, methods of parameter sweep computations, and methods of the data analysis. We develop the toolkit named SIRIUS III for the simulation modeling of queue systems using high-performance computing. In addition, we design a conceptual model that represents knowledge about both the subject domain of solved problems and computing environment. Using the proposed toolkit, we solved a number of problems related to warehouse logistics. Computational experiments showed the high scalability of parameter sweep computations on heterogeneous resources.

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“…Nowadays, projects related to developing and applying specialized environments for distributed computing in various subject domains are highly relevant. Widely known examples of such environments are the follows ones:  Distributed Grid-infrastructure for supporting experiments in the field of high-energy physics at the accelerator of the Large Hadron Collider [6],  Open web-based platform for genomic research [7],  Cloud platform for rapid analysis of biomedical data [8],  High-level toolkit for the development of distributed scientific applications [9],  E-Science infrastructure for data-driven computing [10],  Service-oriented distributed computing environment for simulation modeling warehouse logistics [11],  System for modeling critical infrastructures of the electric power industry in a distributed computing environment [3]. Usually, the problem-solving scheme in such environments is closely correlated with the workflow concept that is used in describing the interrelated subproblems.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Distributed Computing Environment for Vulnerability Analysis of Energy Critical Infrastructures

Edelev¹,

Sidorov

Feoktistov

2018

Proceedings of the VTH International Workshop "Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clou

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In the paper, we propose a new approach to developing the subject-oriented heterogeneous distributed computing environment for the vulnerability analysis of the energy critical infrastructures of the national level. We provide a framework for creating such environment and developing applied software packages for the vulnerability analysis. Unlike the known tools for developing subject-oriented environments, our framework ensures integrating the models of Grid and cloud computing, as well as selecting the optimal environment resource configuration for performing experiments of various scales.

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Simulation modeling in heterogeneous distributed computing environments to support decisions making in warehouse logistics

Cited by 14 publications

References 8 publications

Predicting runtime of computational jobs in distributed computing environment

Predicting runtime of computational jobs in distributed computing environment

Toolkit for simulation modeling of queue systems in Grid

Heterogeneous Distributed Computing Environment for Vulnerability Analysis of Energy Critical Infrastructures

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