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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud
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KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters
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RIFLING: A reinforcement learning‐based GPU scheduler for deep learning research and development platforms
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Scheduling algorithms for truly heterogeneous hierarchical fog networks
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An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment
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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
inferredcites →https://doi.org/10.1007/s10586-021-03371-8openalex-snapshot
Multi-objective Task Scheduling in cloud-fog computing using goal programming approach
inferredcites →https://doi.org/10.1007/s11227-020-03427-3openalex-snapshot
KCSS: Kubernetes container scheduling strategy
inferredcites →https://doi.org/10.1016/j.cie.2020.107024openalex-snapshot
Heuristic algorithms for solving a set of NP-hard single-machine scheduling problems with resource-dependent processing times
inferredcites →https://doi.org/10.1016/j.cor.2007.02.014openalex-snapshot
A genetic algorithm for the Flexible Job-shop Scheduling Problem
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/ic2e52221.2021openalex-snapshot
2021 IEEE International Conference on Cloud Engineering (IC2E)
inferredcites →https://doi.org/10.1109/icdcs.2017.123openalex-snapshot
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/icde53745.2022.00209openalex-snapshot
Fluid: Dataset Abstraction and Elastic Acceleration for Cloud-native Deep Learning Training Jobs
inferredcites →https://doi.org/10.1109/infocom42981.2021.9488670openalex-snapshot
NetMARKS: Network Metrics-AwaRe Kubernetes Scheduler Powered by Service Mesh
inferredcites →https://doi.org/10.1109/infocom42981.2021.9488701openalex-snapshot
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
inferredcites →https://doi.org/10.1109/ipdps49936.2021.00102openalex-snapshot
QoS-Aware and Resource Efficient Microservice Deployment in Cloud-Edge Continuum
inferredcites →https://doi.org/10.1109/jiot.2020.3025015openalex-snapshot
Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning
inferredcites →https://doi.org/10.1109/mnet.011.2000392openalex-snapshot
AI-Based Resource Management in Beyond 5G Cloud Native Environment
inferredcites →https://doi.org/10.1109/tc.2013.148openalex-snapshot
Adaptive Resource Provisioning for the Cloud Using Online Bin Packing
inferredcites →https://doi.org/10.1109/tpds.2021.3128037openalex-snapshot
Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum
inferredcites →https://doi.org/10.1109/tpds.71openalex-snapshot
IEEE Transactions on Parallel and Distributed Systems
inferredcites →https://doi.org/10.1145/2670518openalex-snapshot
Proceedings of the 13th ACM Workshop on Hot Topics in Networks
inferredcites →https://doi.org/10.1145/3005745.3005750openalex-snapshot
Resource Management with Deep Reinforcement Learning
inferredcites →https://doi.org/10.1145/3163058openalex-snapshot
Proceedings of the 2017 7th International Conference on Communication and Network Security
inferredcites →https://doi.org/10.1145/3267809openalex-snapshot
Proceedings of the ACM Symposium on Cloud Computing
inferredcites →https://doi.org/10.1145/3290480.3290507openalex-snapshot
Research on Kubernetes' Resource Scheduling Scheme
inferredcites →https://doi.org/10.1145/3341302openalex-snapshot
Proceedings of the ACM Special Interest Group on Data Communication
inferredcites →https://doi.org/10.1145/3341302.3342080openalex-snapshot
Learning scheduling algorithms for data processing clusters
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/10.1609/aaai.v30i1.10295openalex-snapshot
Deep Reinforcement Learning with Double Q-Learning
inferredcites →https://doi.org/10.1609/aaai.v32i1.11796openalex-snapshot
Rainbow: Combining Improvements in Deep Reinforcement Learning
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An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm
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A Task Scheduling Algorithm for Hadoop Platform
inferredcites →https://doi.org/10.5555/1972457.1972488openalex-snapshot
Mesos: a platform for fine-grained resource sharing in the data center
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Sustainable, Reliability-Score-Aware Elastic Orchestration for Edge Devices
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Advances in Kubernetes Scheduling for Efficient Resource Utilization and Environmental Impact Mitigation: A Review
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KubeAI-An AI-Powered Pod Placement Scheduler for Kubernetes
inferred← citeshttps://doi.org/10.1109/cisat66811.2025.11181794openalex-snapshot
Autonomous Resource Management in Microservice Systems via Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/cloud62652.2024.00020openalex-snapshot
Telemetry-Driven Microservices Orchestration in Cloud-Edge Environments
inferred← citeshttps://doi.org/10.1109/cloud67622.2025.00040openalex-snapshot
SLO-Aware Container Orchestration on Kubernetes Clusters
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A Reliability Score for Video-Analytics Edge-IoT Devices in Urban Environments
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Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach
inferred← citeshttps://doi.org/10.1109/icccbda61447.2024.10569976openalex-snapshot
CAROKRS: Cost-Aware Resource Optimization Kubernetes Resource Scheduler
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SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
inferred← citeshttps://doi.org/10.1109/icfec69006.2026.00015openalex-snapshot
NL-CPS: Reinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters
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Data-Connector: An Agent-Based Framework for Autonomous ML-Based Smart Management in Cloud-Edge Continuum
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NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks
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ODRL: Reinforcement Learning in Priority Scheduling for Running Cost Optimization
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DQKS: A Pluggable Kubernetes Scheduler Based on the DDQN Algorithm
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Admission Controller Model Based on Reinforcement Learning for Kubernetes Security with Proven PAC Guarantees
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Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform
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Gymnasium Library Interface for Multi Access Edge Computing
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DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment
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Microservice Orchestration With Database for Delay-Sensitive Applications in Cloud–Edge Networks via Deep Reinforcement Learning
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Defragmentation Scheduling with Deep Reinforcement Learning in Shared GPU Clusters
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A Kubernetes custom scheduler based on reinforcement learning for compute‑intensive pods
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Multi-objective formulation of efficient microservice deployment in Kubernetes with dynamic resource allocation using hybrid nature-inspired algorithm
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3323844/v1openalex-snapshot
ODRL: Application of Reinforcement Learning in Priority Scheduling for Running Cost Optimization
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DEVELOPMENT OF AN ACCESS CONTROLLER MODEL FOR KUBERNETES SECURITY WITH PROVABLE PAC GUARANTEES BASED ON ENTROPY
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AI-Native Infrastructure for the Autonomous Enterprise: Advancing Self-Optimizing Database, Big Data, and Cloud Ecosystems
inferred← citeshttps://doi.org/10.3390/computers14120560openalex-snapshot
Energy-Efficient Container Scheduling Based on Deep Reinforcement Learning in Data Centers
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Optimizing Kubernetes Scheduling for Web Applications Using Machine Learning
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EPRS: Experience-Prioritized Reinforcement Scheduler in Edge Clusters
inferred← citeshttps://doi.org/10.55524/ijircst.2026.14.4.4openalex-snapshot
Confidence-Gated Adaptive Routing (CGAR): A Bandit-Theoretic Approach to the Cold-Start Problem in Reinforcement-Learning-Based API Gateways
inferred← citeshttps://doi.org/10.7717/peerj-cs.3964openalex-snapshot
Bridging FinOps practice and machine learning research: a systematic review of cloud cost optimization
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