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KubeSC‐RTP: Smart scheduler for Kubernetes platform on CPU‐GPU heterogeneous systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/spe.2898openalex-snapshot
KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters
inferredcites →https://doi.org/10.1007/978-1-4419-6524-0openalex-snapshot
Handbook of Cloud Computing
inferredcites →https://doi.org/10.1007/978-3-030-51965-0_50openalex-snapshot
A Proposal of Kubernetes Scheduler Using Machine-Learning on CPU/GPU Cluster
inferredcites →https://doi.org/10.1007/978-3-319-68066-8_13openalex-snapshot
Client-Side Scheduling Based on Application Characterization on Kubernetes
inferredcites →https://doi.org/10.1007/s11227-018-2435-1openalex-snapshot
E-OSched: a load balancing scheduler for heterogeneous multicores
inferredcites →https://doi.org/10.1016/j.jpdc.2019.05.015openalex-snapshot
Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system
inferredcites →https://doi.org/10.1080/15228053.2020.1766224openalex-snapshot
The Hundred-Page Machine Learning Book
inferredcites →https://doi.org/10.1109/eiconrusnw.2016.7448189openalex-snapshot
The scheduling based on machine learning for heterogeneous CPU/GPU systems
inferredcites →https://doi.org/10.1109/hipc.2014.7116910openalex-snapshot
Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms
inferredcites →https://doi.org/10.1109/icc.2019.8762053openalex-snapshot
Container Orchestration Engines: A Thorough Functional and Performance Comparison
inferredcites →https://doi.org/10.1109/mcc.2014.51openalex-snapshot
Containers and Cloud: From LXC to Docker to Kubernetes
inferredcites →https://doi.org/10.1145/2996890.3007869openalex-snapshot
Modelling performance & resource management in kubernetes
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.8230openalex-snapshot
Equalizer: Energy‐efficient machine learning‐based heterogeneous cluster load balancer
inferred← citeshttps://doi.org/10.1002/cpe.8303openalex-snapshot
Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization
inferred← citeshttps://doi.org/10.1002/cpe.8318openalex-snapshot
A Survey on Heterogeneous CPU–GPU Architectures and Simulators
inferred← citeshttps://doi.org/10.1007/978-3-032-11946-9_26openalex-snapshot
CPUSched-Forecast: Enhancing Scheduling via Time Series Prediction
inferred← citeshttps://doi.org/10.1007/s00607-024-01369-9openalex-snapshot
GPU cluster dynamics: insights from Alibaba’s 2023 trace release
inferred← citeshttps://doi.org/10.1007/s10586-023-04215-3openalex-snapshot
Machine learning-driven energy-efficient load balancing for real-time heterogeneous systems
inferred← citeshttps://doi.org/10.1016/j.comcom.2025.108235openalex-snapshot
Multi-dimensional resource placement algorithm based on parallel genetic algorithm
inferred← citeshttps://doi.org/10.1016/j.future.2026.108428openalex-snapshot
Real-time AI-powered monitoring for energy-efficient scheduling in multi-node heterogeneous systems
inferred← citeshttps://doi.org/10.1016/j.procs.2025.09.341openalex-snapshot
Semantic Feedback Processing with LLMs: Automating Issue Detection and Prioritization in DevOps
inferred← citeshttps://doi.org/10.1016/j.sysarc.2025.103583openalex-snapshot
Scheduling virtual machines and containers: A comparative review of techniques, performance, and future trends
inferred← citeshttps://doi.org/10.1109/3ict56508.2022.9990623openalex-snapshot
HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems
inferred← citeshttps://doi.org/10.1109/access.2023.3313643openalex-snapshot
K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP
inferred← citeshttps://doi.org/10.1109/icsca57840.2023.10087604openalex-snapshot
RTLB_Sched: Real Time Load Balancing Scheduler for CPU-GPU Heterogeneous Systems
inferred← citeshttps://doi.org/10.1109/tsc.2026.3672439openalex-snapshot
Microservice Scheduling With Spatiotemporal Learning in Dynamic and GPU-CPU Heterogeneous Edge Environment
inferred← citeshttps://doi.org/10.1186/s13677-025-00737-wopenalex-snapshot
Functionality-aware offloading technique for scheduling containerized edge applications in IoT edge computing
inferred← citeshttps://doi.org/10.32604/cmc.2025.061182openalex-snapshot
GPU Usage Time-Based Ordering Management Technique for Tasks Execution to Prevent Running Failures of GPU Tasks in Container Environments
inferred← citeshttps://doi.org/10.3390/electronics14051048openalex-snapshot
A Survey of Advancements in Scheduling Techniques for Efficient Deep Learning Computations on GPUs
inferred← citeshttps://doi.org/10.3390/math11204269openalex-snapshot
Optimization of Task-Scheduling Strategy in Edge Kubernetes Clusters Based on Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.3390/s24072158openalex-snapshot
Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework
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