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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | ML-Enhanced Control Planes for Adaptive Distributed Systems |
| Authors Name: | Ramkinker Singh |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205599
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| Published Paper Id: | IJRTI2508002 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i8.205599 |
| Abstract: | The growth of adaptive distributed systems requires smarter and more resilient control planes that adapt to system behaviors and failures. Machine Learning (ML) is becoming an important enabler for advancing these control planes' capabilities with predictive analytics, failure detection, energy efficiency, and efficient data routing. This paper reviews recent research on ML-enabled control planes in adaptive distributed system contexts, including fault tolerance, data replication, energy management, optical networking, and security. The authors systematically reviewed some of the most significant research articles in these areas, and discuss how ML can be embedded within different layers of control plane architecture, and its consequent impact on performance, scalability, reliability, and so on. The review points out important trends, issues, and technology that impact how control planes are developing and incorporates some aspect of reinforcement learning, active learning, and root cause analysis frameworks. The review also reinforces the ability of ML to make distributed systems resilient and context aware, as well as the important need for resilient, secure ML models. In contrast to other reviews that have pointed out the power of machine learning in disconnect domains, this paper takes an integrated view that correlates fault tolerance, the energy efficiency and security and optical networks with ML-enabled control planes—beginning to fill a significant gap in the literature with respect to adaptive distributed systems and supporting a holistic view. The insights also serve as a lead into direction for future research with respect to self-optimizing and secure machine learning-based control systems. |
| Keywords: | Machine Learning, Adaptive Systems, Distributed Control Planes, Fault Tolerance |
| Cite Article: | "ML-Enhanced Control Planes for Adaptive Distributed Systems", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a5-a10, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508002.pdf |
| Downloads: | 000807 |
| ISSN: |
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: IJRTI2508002
Registration ID:205599
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i8.205599
Page No: a5-a10 Country: Hayward, California, United States Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508002 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508002 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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