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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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| Paper Title: | Optimizing Spark History Server for Large-Scale ML Workloads in Financial Systems |
| Authors Name: | Prasanth Sasidharan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_208213
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| Published Paper Id: | IJRTI2602126 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i2.208213 |
| Abstract: | Even basic procedures of a financial apparatus like fraud detection, credit scoring, and risk modelling utilizing machine learning (ML) processes are extremely complex, and the versatility of data infrastructure is put in the limelight. The Apache Spark platform of big data is now fashionable for executing big ML loads, owing to the fact that processing has been made in-memory and the ecosystem has been extended. Nevertheless, the performance and observability of every component—which in most cases is a bottleneck—is the Spark History Server (SHS), which is incidentally the component involved in post-execution diagnosis and monitoring. High-frequency financial settings may lead to low SHS performance and difficulty in the speed of troubleshooting, auditability, and understandability of the systems. The critical analysis of the ways through which the Spark History Server can be optimized with references to large-scale operations in financial systems connected to ML is devoted to the review. The paper is based on the literature available in the sphere of distributed computing, cloud infrastructure, hybrid data processing pipelines, and real-time analytics, and proposes architectural, operational, and performance-based solutions to SHS improvement. Among the most important ones are log compaction, memory management, streaming integration, and the importance of resilient cloud environments as tools to scale SHS. The review and recommendations of how this can be improved in the future also cover future limitations to implementations of SHS. Financial institutions will have an opportunity to increase the speed of their working activity, address new requirements, and provide the further delivery of ML-based insights to the further improvement of SHS functionality. |
| Keywords: | Spark History Server, Machine Learning, Financial Systems, Log Optimization |
| Cite Article: | "Optimizing Spark History Server for Large-Scale ML Workloads in Financial Systems", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.b208-b213, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602126.pdf |
| Downloads: | 00096 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2602126
Registration ID:208213
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i2.208213
Page No: b208-b213 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602126 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602126 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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