Computation Time Optimization Strategies for Critical Infrastructure Simulator

Computation Time Optimization Strategies for Critical Infrastructure Simulator

Stať ve sborníku v databázi WoS či Scopus

This paper investigates the computation time optimization of a software tool for critical infrastructure simulation. First, modifications to the input data processing module are presented to increase the efficiency of data retrieval and reduce processing time. Next, the effect of different data formats on computational time and memory load is analyzed. Tests showed that some formats provided significant improvements in data processing speed and reductions in data file size. The next step was the deployment of a software tool in the supercomputing infrastructure, which allowed the optimization of computation time through orchestration in Kubernetes. The modified architecture of the software tool allows efficient use of computing power from the computing center only for running simulations, while administrative tasks are performed on the user’s local device. This integration into the supercomputing infrastructure provides the ability to run the software tool on different devices with sufficient computing power as needed.

This paper investigates the computation time optimization of a software tool for critical infrastructure simulation. First, modifications to the input data processing module are presented to increase the efficiency of data retrieval and reduce processing time. Next, the effect of different data formats on computational time and memory load is analyzed. Tests showed that some formats provided significant improvements in data processing speed and reductions in data file size. The next step was the deployment of a software tool in the supercomputing infrastructure, which allowed the optimization of computation time through orchestration in Kubernetes. The modified architecture of the software tool allows efficient use of computing power from the computing center only for running simulations, while administrative tasks are performed on the user’s local device. This integration into the supercomputing infrastructure provides the ability to run the software tool on different devices with sufficient computing power as needed.

power grid; distribution network; smart grid; simulation; optimization; computation time

power grid; distribution network; smart grid; simulation; optimization; computation time

VRTAL, M.; BENEDIKT, J.; FUJDIAK, R.; PRAKS, P.; TOMAN, P.

2026

13.09.2024

Technical University of Kosice

Košice, Slovak Republic

978-3-031-97332-1

Research and Developments in Electrical Power Engineering, Proceedings of the 12th International Scientific Symposium on Electrical Power Engineering, Elektroenergetika 2024, September 11–13, 2024, Stará Lesná, Slovakia

Lecture Notes in Electrical Engineering 1446

2024

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