Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-based Energy Infrastructure Resilience Studies

Alexander Feoktistov
* ORCID logo ,
Alexei Edelev
,
Andrei Tchernykh
ORCID logo ,
Sergey Gorsky
ORCID logo ,
Olga Basharina
,
Evgeniy Fereferov
ORCID logo
Version 1 : Received: 1 November 2023 / Approved: 1 November 2023 / Online: 2 November 2023 (00:23:09 CET)

A peer-reviewed article of this Preprint also exists.

Feoktistov, A.; Edelev, A.; Tchernykh, A.; Gorsky, S.; Basharina, O.; Fereferov, E. An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-Based Energy Infrastructure Resilience Studies. Computation 2023, 11, 243. Feoktistov, A.; Edelev, A.; Tchernykh, A.; Gorsky, S.; Basharina, O.; Fereferov, E. An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-Based Energy Infrastructure Resilience Studies. Computation 2023, 11, 243.

Abstract

Implementing High-Performance Computing to solve In-Memory Data Grid (IMDG)-based energy infrastructure resilience research problems in a heterogeneous environment presents a challenge to workflow management systems. Large-scale energy infrastructure needs multi-variant planning and tools to allocate and dispatch resources in IMDG taking into account the subject domain specificity, resource characteristics, and constraints and quotas for resource use. To that end, we propose a workflow management system using our Orlando Tools framework. To scale computing resources, we provide their integration and use corresponding software to determine key application parameters that can significantly impact the processed data size and the required number of allocated resources. We automated the IMDG cluster launch for the workflow executions. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our approach explores large infrastructures by modeling many simultaneously failed elements of different types up to the number of network elements. In terms of problem-solving efficiency and resource use, we achieve near-linear speedup with increasing the number of nodes of each resource.

Keywords

energy systems; resilience; vulnerability; high-performance computing; IMDG; scientific workflows

Subject

Computer Science and Mathematics, Computer Science

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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