On the public citation graph

Optimization of Task-Scheduling Strategy in Edge Kubernetes Clusters Based on Deep Reinforcement Learning

https://doi.org/10.3390/math11204269

0 signed · 37 inferred · view as graph

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.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.7108openalex-snapshot

KubeSC‐RTP: Smart scheduler for Kubernetes platform on CPU‐GPU heterogeneous systems

inferredcites →https://doi.org/10.1007/978-3-030-91431-8_36openalex-snapshot

Deep Reinforcement Learning Based Task Offloading Strategy Under Dynamic Pricing in Edge Computing

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/10.1007/978-981-99-1252-0_82openalex-snapshot

Actual Resource Usage-Based Container Scheduler for High Resource Utilization

inferredcites →https://doi.org/10.1007/s11227-020-03427-3openalex-snapshot

KCSS: Kubernetes container scheduling strategy

inferredcites →https://doi.org/10.1016/j.eswa.2022.119077openalex-snapshot

Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem

inferredcites →https://doi.org/10.1016/j.jocs.2022.101840openalex-snapshot

A PSO task scheduling and IT2FCM fuzzy data placement strategy for scientific cloud workflows

inferredcites →https://doi.org/10.1016/j.swevo.2023.101312openalex-snapshot

A parallel heuristic for hybrid job shop scheduling problem considering conflict-free AGV routing

inferredcites →https://doi.org/10.1016/j.sysarc.2022.102780openalex-snapshot

Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach

inferredcites →https://doi.org/10.1109/ccgrid51090.2021.00097openalex-snapshot

A Reinforcement Learning Approach to Reduce Serverless Function Cold Start Frequency

inferredcites →https://doi.org/10.1109/ic2e48712.2020.00019openalex-snapshot

RLSK: A Job Scheduler for Federated Kubernetes Clusters based on Reinforcement Learning

inferredcites →https://doi.org/10.1109/jiot.2023.3244545openalex-snapshot

Delay-Aware Container Scheduling in Kubernetes

inferredcites →https://doi.org/10.1109/msp.2017.2743240openalex-snapshot

Deep Reinforcement Learning: A Brief Survey

inferredcites →https://doi.org/10.1109/noms.2018.8406321openalex-snapshot

Fogernetes: Deployment and management of fog computing applications

inferredcites →https://doi.org/10.1109/tcomm.2020.3007742openalex-snapshot

Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference

inferredcites →https://doi.org/10.1109/tnse.2022.3217311openalex-snapshot

DeepEdge: A Deep Reinforcement Learning Based Task Orchestrator for Edge Computing

inferredcites →https://doi.org/10.1109/tpds.2021.3052895openalex-snapshot

DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters

inferredcites →https://doi.org/10.1109/twc.2022.3153316openalex-snapshot

Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

inferredcites →https://doi.org/10.1145/3539606openalex-snapshot

Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges

inferredcites →https://doi.org/10.1145/3544788openalex-snapshot

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

inferredcites →https://doi.org/10.3390/electronics12183882openalex-snapshot

A DRL-Based Task Offloading Scheme for Server Decision-Making in Multi-Access Edge Computing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-24513-7_3openalex-snapshot

Advances in Kubernetes Scheduling for Efficient Resource Utilization and Environmental Impact Mitigation: A Review

inferred← citeshttps://doi.org/10.1007/978-3-032-31526-7_40openalex-snapshot

AI/ML-Driven Kubernetes Scheduling for Energy Efficiency and Carbon Footprint Reduction: A Comprehensive Review

inferred← citeshttps://doi.org/10.1109/access.2024.3520422openalex-snapshot

Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency

inferred← citeshttps://doi.org/10.1109/access.2026.3696055openalex-snapshot

KubeAI-An AI-Powered Pod Placement Scheduler for Kubernetes

inferred← citeshttps://doi.org/10.1109/iaict71158.2026.11620968openalex-snapshot

Implementation of the Fremer Model to Optimize Kubernetes Configuration in Concert Ticketing Application Deployment

inferred← citeshttps://doi.org/10.1109/ic3se62002.2024.10593075openalex-snapshot

Efficient Load Balancing Algorithms for Edge Computing in IoT Environments

inferred← citeshttps://doi.org/10.1109/iccbi68589.2026.11619675openalex-snapshot

Adaptive Cloud Data Architecture for Autonomous Pipeline Orchestration using Generative AI

inferred← citeshttps://doi.org/10.1109/icfec69006.2026.00015openalex-snapshot

NL-CPS: Reinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters

inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11229358openalex-snapshot

H2C-Scheduler: An Efficient and Scalable Job Scheduling Method for Hybrid Heterogeneous AI Computing Clusters

inferred← citeshttps://doi.org/10.1109/is61756.2024.10705177openalex-snapshot

Bottom-Up Resource Orchestration in Edge Computing: An Agent-Based Modeling Approach

inferred← citeshttps://doi.org/10.1109/isvlsi65124.2025.11130293openalex-snapshot

A Novel Architecture using Dynamic Computing Nodes to Improve Multi-Access Edge Cluster Productivity

inferred← citeshttps://doi.org/10.2139/ssrn.5380276openalex-snapshot

Cluster Workload Allocation: A Hybrid Heuristic and Transfer Learning Approach

inferred← citeshttps://doi.org/10.3390/ai7090347openalex-snapshot

Accountable Autonomy: A Governance Framework for Agentic AI in Telecommunication Networks

inferred← citeshttps://doi.org/10.3390/computers14120560openalex-snapshot

Energy-Efficient Container Scheduling Based on Deep Reinforcement Learning in Data Centers

inferred← citeshttps://doi.org/10.3390/math13081342openalex-snapshot

Intelligent Dynamic Multi-Dimensional Heterogeneous Resource Scheduling Optimization Strategy Based on Kubernetes

inferred← citeshttps://doi.org/10.3390/math14132414openalex-snapshot

DEVS-Based Simulation of Cube-Shaped AS/RS: Demand-Driven Digging Minimization and Cooperative Multi-AGV Predictive Staging

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.