Data Processing in Problem-Solving of Energy System Vulnerability Based on In-memory Data Grid
Nowadays, data analysis is an integral part of large-scale scientific and applied experiments. Opportunities of modern computing environments allow us to move from operating with traditional storage systems within solving data-intensive problems to the in-memory data grid technologies. Such technologies improve the performance and scalability of data processing compared to external databases because of a faster random access memory and other hardware advancements. The considered data grid enables applications to cache data in the memory. Based on our practical experience, we discuss the advantages of applying the in-memory data grid technology to analyze the energy system vulnerability. The complexity of this problem is determined by considering possible combinations of simultaneous failures of energy system elements. We use open source-based Apache Ignite to support high-performance computing and data distribution. The study aims to evaluate the impact of the data grid scaling on the problem-solving quality criteria. We used the resources of the public access Irkutsk Supercomputer Center to carry out experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Библиографическая ссылка
Gorsky S., Edelev A., Feoktistov A. Data Processing in Problem-Solving of Energy System Vulnerability Based on In-memory Data Grid // Lecture Notes in Networks and Systems. Vol.424. 2022. P.271-279. ISBN (print): 9783030970192. DOI: 10.1007/978-3-030-97020-8_25
