Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments

Tchernykh, Andrei; Feoktistov, Alexander; Gorsky, Sergey; Sidorov, Ivan; Kostromin, Roman; Bychkov, I. V.; Basharina, Olga; Alexandrov, Vassil N.; Rivera-Rodríguez, Raúl

doi:10.1007/978-3-030-16205-4_20

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…The systems supported the package design and use, for example, Orlando Tools [22], provide mapping jobs to target resources based on methods for computation planning and resource allocation. Wherein, the selection of resources is a key issue to ensure the efficiency of their use and decrease job makespan.…”

Section: Environmentmentioning

confidence: 99%

Tender of computational works in heterogeneous distributed environment

Feoktistov¹

2020

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

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Nowadays, applying various market-based methods for regulating supply and demand of resources for distributed computing is becoming increasingly relevant. In particular, different forms of standard auctions are actively used. However, their basic capabilities often do not enable to fully solve the complicated problems of resource allocation in a heterogeneous distributed computing environment. In this regard, a tender of computational works based on a combinatorial Vickrey auction has been designed. It is applied within multi-agent computing management. For the tender, new models are proposed to rank the criteria of resources owners and users. The tender use advantages are shown in comparison with traditional meta-schedulers.

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Section: Environmentmentioning

confidence: 99%

Tender of computational works in heterogeneous distributed environment

Feoktistov¹

2020

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

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“…As an example, we consider the problem of improving the processes of loading and unloading of goods in a warehouse through their simulation. 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].…”

Section: Examplementioning

confidence: 99%

“…The experiments were carried out on two different clusters: PC-cluster and HPC-cluster. The characteristics of both clusters are provided in [17]. Figure 3 and Figure 4 show the actual and predicted jobs runtime on the PC-cluster and HPC-cluster, respectively.…”

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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“…Unlike other tools for developing scientific applications, Orlando Tools supports the intensive evolution of algorithmic knowledge, adaptation of existed and designing new ones. It automates the non-trivial technological sequence of the collaborative development and use of packages including the continuous integration, delivery, deployment, and execution of package modules in a heterogeneous distributed environment [18][19][20][21].…”

Section: Scientific Application For Analyzing the Energy System Vulnementioning

confidence: 99%

Large-scale analysis of energy system vulnerability using in-memory data grid

Edelev¹,

Sidorov²,

Gorsky³

et al. 2020

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

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Nowadays, determining critical components of energy systems is a relevant problem. The complexity of its solving increases significantly when it is necessary to take into account the simultaneous failures of such components. Usually, in problem-solving, processing a large number of failure variants and their consequences is required. Processing such data using traditional relational database management systems does not allow us to quickly identify the most critical components. In the paper, our successful practical experience in applying an in-memory data grid within large-scale analyzing of the energy system vulnerability is provided. The experimental analysis showed the good scalability of distributed computing and significant reduction in data processing time compared to using an open-source SQL relational database management system. In developing and applying the distributed applied software package for solving the aforementioned problem we have used the Orlando Tools framework. Within its applying, we have implemented continuous integration of the package software taking into account the preparing and processing of subject-oriented data through the in-memory data grid.

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Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments

Cited by 8 publications

References 18 publications

Tender of computational works in heterogeneous distributed environment

Tender of computational works in heterogeneous distributed environment

Predicting runtime of computational jobs in distributed computing environment

Large-scale analysis of energy system vulnerability using in-memory data grid

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