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KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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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.2660openalex-snapshot
Container‐based cluster orchestration systems: A taxonomy and future directions
inferredcites →https://doi.org/10.1002/spe.2729openalex-snapshot
Straddling the crevasse: A review of microservice software architecture foundations and recent advancements
inferredcites →https://doi.org/10.1007/978-3-319-27122-4_35openalex-snapshot
Multitask Oriented GPU Resource Sharing and Virtualization in Cloud Environment
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/10.1007/978-3-540-92990-1_4openalex-snapshot
Predictive Runtime Code Scheduling for Heterogeneous Architectures
inferredcites →https://doi.org/10.1007/s11227-013-0870-6openalex-snapshot
An efficient scheduling scheme using estimated execution time for heterogeneous computing systems
inferredcites →https://doi.org/10.1109/cluster.2011.46openalex-snapshot
Symphony: A Scheduler for Client-Server Applications on Coprocessor-Based Heterogeneous Clusters
inferredcites →https://doi.org/10.1109/comst.2017.2682318openalex-snapshot
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
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/hpcs.2010.5547126openalex-snapshot
rCUDA: Reducing the number of GPU-based accelerators in high performance clusters
inferredcites →https://doi.org/10.1109/ipdpsw.2013.102openalex-snapshot
Dynamic Sharing of GPUs in Cloud Systems
inferredcites →https://doi.org/10.1109/ispass.2015.7095802openalex-snapshot
An updated performance comparison of virtual machines and Linux containers
inferredcites →https://doi.org/10.1109/mc.2017.3641638openalex-snapshot
Real-Time Video Analytics: The Killer App for Edge Computing
inferredcites →https://doi.org/10.1109/mcc.2014.51openalex-snapshot
Containers and Cloud: From LXC to Docker to Kubernetes
inferredcites →https://doi.org/10.1109/netgames.2015.7382987openalex-snapshot
Virtual machines vs. containers in cloud gaming systems
inferredcites →https://doi.org/10.1109/sc2.2017.24openalex-snapshot
A New Docker Swarm Scheduling Strategy
inferredcites →https://doi.org/10.1109/tc.2011.112openalex-snapshot
vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines
inferredcites →https://doi.org/10.1145/1519138.1519141openalex-snapshot
GViM
inferredcites →https://doi.org/10.1145/2996890.3007869openalex-snapshot
Modelling performance & resource management in kubernetes
inferredcites →https://doi.org/10.1145/3068281openalex-snapshot
GPU Virtualization and Scheduling Methods
inferredcites →https://doi.org/10.1145/3344341.3368811openalex-snapshot
FogDocker

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.7108openalex-snapshot
KubeSC‐RTP: Smart scheduler for Kubernetes platform on CPU‐GPU heterogeneous systems
inferred← citeshttps://doi.org/10.1002/cpe.8344openalex-snapshot
Dynamic Load Balancing in Kubernetes Environments With Kubernetes Scheduling Extension ( KSE )
inferred← citeshttps://doi.org/10.1002/spe.3284openalex-snapshot
DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system
inferred← citeshttps://doi.org/10.1002/spe.70000openalex-snapshot
Kubernetes in Action: Exploring the Performance of Kubernetes Distributions in the Cloud
inferred← citeshttps://doi.org/10.1002/spe.70073openalex-snapshot
ML ‐Driven DevOps : An Empirical Framework for Predictive Optimization and Intelligent Automation in CI / CD Pipelines
inferred← citeshttps://doi.org/10.1007/s10723-024-09788-wopenalex-snapshot
Energy-aware Scheduling Algorithm for Microservices in Kubernetes Clouds
inferred← citeshttps://doi.org/10.1007/s10922-022-09699-9openalex-snapshot
Enabling the Rescheduling of Containerized Workloads in an Ad Hoc Cross-Organizational Collaboration
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/10.1063/5.0287021openalex-snapshot
Using cloud services, docker containers, and kubernetes for efficient digital AI development
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/agro-geoinformatics59224.2023.10233398openalex-snapshot
Distributed Satellite Pod Scheduling Algorithm Based on DQN
inferred← citeshttps://doi.org/10.1109/auteee62881.2024.10869772openalex-snapshot
A DRL-Based Cluster Scheduling Algorithm Configuration Optimization Approach for Artificial Intelligence Jobs
inferred← citeshttps://doi.org/10.1109/cars67163.2025.11337848openalex-snapshot
Performance Study on A Kubernetes Based Learning Platform for AI Education and Workforce Training
inferred← citeshttps://doi.org/10.1109/globecom52923.2024.10901331openalex-snapshot
Kubernetes Scheduling Design Based on Imitation Learning in Edge Cloud Scenarios
inferred← citeshttps://doi.org/10.1109/iceib57887.2023.10170110openalex-snapshot
Enhancing Node Fault Tolerance through High-Availability Clusters in Kubernetes
inferred← citeshttps://doi.org/10.1109/icise-ie64355.2024.11025501openalex-snapshot
A Design of Storage-computation Separation Architecture for Cloud-native Environments
inferred← citeshttps://doi.org/10.1109/iros58592.2024.10802072openalex-snapshot
Optimizing Kubernetes Deployment of Robotic Applications with HEFT-based Container Orchestration
inferred← citeshttps://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00025openalex-snapshot
KubeHICE: Performance-aware Container Orchestration on Heterogeneous-ISA Architectures in Cloud-Edge Platforms
inferred← citeshttps://doi.org/10.1109/ispa67752.2025.00030openalex-snapshot
OLASS: Online Lyapunov Optimization with Selective Scheduling in High-Load Edge Computing
inferred← citeshttps://doi.org/10.1109/jcc56315.2022.00017openalex-snapshot
ProxyDWRR: A Dynamic Load Balancing Approach for Heterogeneous-CPU Kubernetes Clusters
inferred← citeshttps://doi.org/10.1145/3539606openalex-snapshot
Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges
inferred← citeshttps://doi.org/10.1145/3544788openalex-snapshot
Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches
inferred← citeshttps://doi.org/10.1145/3617123openalex-snapshot
Providing Realtime Support for Containerized Edge Services
inferred← citeshttps://doi.org/10.1145/3773274.3774276openalex-snapshot
Energy-Aware Latency Optimization for Scheduling Serverless Workload in Edge Computing Environment
inferred← citeshttps://doi.org/10.1186/s13677-023-00471-1openalex-snapshot
A survey of Kubernetes scheduling algorithms
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/10.21275/sr241201012040openalex-snapshot
Optimization of Kubernetes for the High-Performance Computing with Kubernetes, Performance Analysis, and Dynamic Workload Placement toward the Enhancement of Cloud Computing
inferred← citeshttps://doi.org/10.2139/ssrn.5472730openalex-snapshot
Chang'E: Containerized Heterogeneous Task Scheduling System for Intra-Satellite Embedded XPU and RapidIO
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/app151810098openalex-snapshot
Design of Experiments-Based Adaptive Scheduling in Kubernetes for Performance and Cost Optimization
inferred← citeshttps://doi.org/10.3390/electronics13173494openalex-snapshot
Evaluating ARM and RISC-V Architectures for High-Performance Computing with Docker and Kubernetes
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