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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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Volume 11 | Issue 6
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| Paper Title: | Enhancing GPU Cluster Efficiency in HPC with Kubernetes and Custom Monitoring Agents |
| Authors Name: | Ratan Raj Anandeshi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_212240
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| Published Paper Id: | IJRTI2605166 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i5.212240 |
| Abstract: | GPU clusters are increasingly important in high-performance computing environments for large-scale simulation, data analytics, and deep learning workloads. Meanwhile, Kubernetes has moved beyond cloud-native service orchestration and is increasingly discussed in research and practice as a platform for scientific computing, largely due to its declarative control paradigm, interoperability and complete ecosystem. The key issue here is to balance Kubernetes flexibility and the strict efficiency requirements of GPU-based HPC systems, where the latency sensitivity, topology information, accelerator usage, and fine-grained observability have a strong impact on scientific throughput. This review examines peer-reviewed literature on GPU resource management, container orchestration, telemetry, and monitoring-driven optimization, with particular attention to Kubernetes-based implementations and custom monitoring agents. Themes such as scheduling under heterogeneous accelerator constraints, scientific workload container overhead, node-level and pod-level observability of GPUs, fairness and isolation, and feedback control and performance and energy optimization are major themes. It has been reported that orchestration per se can rarely provide maximum efficiency; quantifiable benefits are more often associated with scheduler extensions, topology-aware placement, and monitoring pipelines which are able to reveal the pressure on memory, streaming multiprocessor occupancy, I/O contention, and thermal or power behaviours. Persistent gaps include the lack of cross-layer metrics, limited support for multi-tenant GPU fragmentation, and insufficient validation at production-scale HPC environments. The discipline is of great importance owing to the fact that the exascale and AI-driven next-generation systems will need to be operating models that combine portability, policy control, and accelerator-aware observability. |
| Keywords: | GPU clusters are increasingly important in high-performance computing environments for large-scale simulation, data analytics, and deep learning workloads. Meanwhile, Kubernetes has moved beyond cloud-native service orchestration and is increasingly discussed in research and practice as a platform for scientific computing, largely due to its declarative control paradigm, interoperability and complete ecosystem. The key issue here is to balance Kubernetes flexibility and the strict efficiency requirements of GPU-based HPC systems, where the latency sensitivity, topology information, accelerator usage, and fine-grained observability have a strong impact on scientific throughput. This review examines peer-reviewed literature on GPU resource management, container orchestration, telemetry, and monitoring-driven optimization, with particular attention to Kubernetes-based implementations and custom monitoring agents. Themes such as scheduling under heterogeneous accelerator constraints, scientific workload container overhead, node-level and pod-level observability of GPUs, fairness and isolation, and feedback control and performance and energy optimization are major themes. It has been reported that orchestration per se can rarely provide maximum efficiency; quantifiable benefits are more often associated with scheduler extensions, topology-aware placement, and monitoring pipelines which are able to reveal the pressure on memory, streaming multiprocessor occupancy, I/O contention, and thermal or power behaviours. Persistent gaps include the lack of cross-layer metrics, limited support for multi-tenant GPU fragmentation, and insufficient validation at production-scale HPC environments. The discipline is of great importance owing to the fact that the exascale and AI-driven next-generation systems will need to be operating models that combine portability, policy control, and accelerator-aware observability. |
| Cite Article: | "Enhancing GPU Cluster Efficiency in HPC with Kubernetes and Custom Monitoring Agents", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b585-b603, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605166.pdf |
| Downloads: | 00098 |
| 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: IJRTI2605166
Registration ID:212240
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i5.212240
Page No: b585-b603 Country: -, -, India Research Area: Arts Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605166 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605166 |
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ISSN: 2456-3315
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