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Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches
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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/ett.3792openalex-snapshot
aTask scheduling approaches in fog computing: A survey
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.2898openalex-snapshot
KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters
inferredcites →https://doi.org/10.1002/spe.3016openalex-snapshot
Container lifecycle‐aware scheduling for serverless computing
inferredcites →https://doi.org/10.1007/978-3-030-05054-2_46openalex-snapshot
GAI: A Centralized Tree-Based Scheduler for Machine Learning Workload in Large Shared Clusters
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/10.1007/978-3-030-58858-8_14openalex-snapshot
Predictable Performance for QoS-Sensitive, Scalable, Multi-tenant Function-as-a-Service Deployments
inferredcites →https://doi.org/10.1007/978-3-030-59851-8_21openalex-snapshot
Interference-Aware Orchestration in Kubernetes
inferredcites →https://doi.org/10.1007/978-3-030-65310-1_37openalex-snapshot
Tail-Latency-Aware Fog Application Replica Placement
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/10.1007/978-3-319-92378-9_14openalex-snapshot
Container Orchestration: A Survey
inferredcites →https://doi.org/10.1007/s11227-020-03427-3openalex-snapshot
KCSS: Kubernetes container scheduling strategy
inferredcites →https://doi.org/10.1007/s11227-021-03623-9openalex-snapshot
GPU-based embedded edge server configuration and offloading for a neural network service
inferredcites →https://doi.org/10.1016/0141-9331(90)90099-hopenalex-snapshot
Stack computers: the new wave
inferredcites →https://doi.org/10.1016/j.comcom.2020.04.061openalex-snapshot
Geo-distributed efficient deployment of containers with Kubernetes
inferredcites →https://doi.org/10.1016/j.future.2018.09.014openalex-snapshot
Task scheduling techniques in cloud computing: A literature survey
inferredcites →https://doi.org/10.1016/j.future.2020.07.017openalex-snapshot
Optimized container scheduling for data-intensive serverless edge computing
inferredcites →https://doi.org/10.1016/j.procs.2020.09.002openalex-snapshot
Dynamic assignment based on a probabilistic matching: Application to server-container assignment
inferredcites →https://doi.org/10.1016/j.proeng.2012.06.337openalex-snapshot
Survey on Scheduling Issues in Cloud Computing
inferredcites →https://doi.org/10.1109/access.2020.3029583openalex-snapshot
ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing
inferredcites →https://doi.org/10.1109/acsos-c51401.2020.00023openalex-snapshot
Toward Interference-aware GPU Container Co-scheduling Learning from Application Profiles
inferredcites →https://doi.org/10.1109/aina15514.2009openalex-snapshot
2009 International Conference on Advanced Information Networking and Applications
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006427openalex-snapshot
Progress-based Container Scheduling for Short-lived Applications in a Kubernetes Cluster
inferredcites →https://doi.org/10.1109/canopie-hpc51917.2020openalex-snapshot
2020 2nd International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC)
inferredcites →https://doi.org/10.1109/cluster49012.2020.00020openalex-snapshot
Co-scheML: Interference-aware Container Co-scheduling Scheme Using Machine Learning Application Profiles for GPU Clusters
inferredcites →https://doi.org/10.1109/comst.2018.2857922openalex-snapshot
Cluster Frameworks for Efficient Scheduling and Resource Allocation in Data Center Networks: A Survey
inferredcites →https://doi.org/10.1109/csci49370.2019.00265openalex-snapshot
Pigeon: A Dynamic and Efficient Serverless and FaaS Framework for Private Cloud
inferredcites →https://doi.org/10.1109/empdp.2019.8671554openalex-snapshot
Heats: Heterogeneity-and Energy-Aware Task-Based Scheduling
inferredcites →https://doi.org/10.1109/etfa.2019.8869219openalex-snapshot
Distributed scheduling in Kubernetes based on MAS for Fog-in-the-loop applications
inferredcites →https://doi.org/10.1109/fmec49853.2020.9144934openalex-snapshot
Latency-Aware Industrial Fog Application Orchestration with Kubernetes
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/icc.2019.8762053openalex-snapshot
Container Orchestration Engines: A Thorough Functional and Performance Comparison
inferredcites →https://doi.org/10.1109/icnp.2019.8888116openalex-snapshot
Container Orchestration by Kubernetes for RDMA Networking
inferredcites →https://doi.org/10.1109/icoin48656.2020.9016481openalex-snapshot
A Container-based Content Delivery Method for Edge Cloud over Wide Area Network
inferredcites →https://doi.org/10.1109/icus50048.2020.9274859openalex-snapshot
A Novel Fined-Grained GPU Sharing Mechanism for Artificial Intelligence Computing-Resource Automatic Scheduling
inferredcites →https://doi.org/10.1109/infocom.2019.8737460openalex-snapshot
Deep Learning-based Job Placement in Distributed Machine Learning Clusters
inferredcites →https://doi.org/10.1109/jiot.2019.2939534openalex-snapshot
KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem
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/netsoft.2019.8806671openalex-snapshot
Towards Network-Aware Resource Provisioning in Kubernetes for Fog Computing Applications
inferredcites →https://doi.org/10.1109/sose.2019.00030openalex-snapshot
Invited Paper: Improving Data Center Efficiency Through Holistic Scheduling In Kubernetes
inferredcites →https://doi.org/10.1109/tcc.2017.2702586openalex-snapshot
Cloud Container Technologies: A State-of-the-Art Review
inferredcites →https://doi.org/10.1109/wf-iot48130.2020openalex-snapshot
2020 IEEE 6th World Forum on Internet of Things (WF-IoT)
inferredcites →https://doi.org/10.1145/3290480.3290507openalex-snapshot
Research on Kubernetes' Resource Scheduling Scheme
inferredcites →https://doi.org/10.1145/3341325.3341992openalex-snapshot
Kubernetes cluster optimization using hybrid shared-state scheduling framework
inferredcites →https://doi.org/10.1145/3342280.3342335openalex-snapshot
Sharpening Kubernetes for the Edge
inferredcites →https://doi.org/10.1145/3400286.3418281openalex-snapshot
Scheduler for Distributed and Collaborative Container Clusters based on Multi-Resource Metric
inferredcites →https://doi.org/10.1145/3407947.3407950openalex-snapshot
A Dynamic I/O Sensing Scheduling Scheme in Kubernetes
inferredcites →https://doi.org/10.1145/3409501.3409503openalex-snapshot
A Scheduler for Serverless Framework base on Kubernetes
inferredcites →https://doi.org/10.1145/3412841.3441887openalex-snapshot
Reducing cold starts during elastic scaling of containers in kubernetes
inferredcites →https://doi.org/10.1186/s13174-020-00129-0openalex-snapshot
QoS-driven scheduling in the cloud
inferredcites →https://doi.org/10.20944/preprints202010.0534.v1openalex-snapshot
SpeCon: Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster
inferredcites →https://doi.org/10.23919/apnoms50412.2020.9236956openalex-snapshot
2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS)
inferredcites →https://doi.org/10.23919/softcom.2019.8903766openalex-snapshot
Context-Aware K8S Scheduler for Real Time Distributed 5G Edge Computing Applications
inferredcites →https://doi.org/10.24138/jcomss.v16i1.1027openalex-snapshot
Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G
inferredcites →https://doi.org/10.3390/app9050931openalex-snapshot
A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks
inferredcites →https://doi.org/10.3390/info12010016openalex-snapshot
Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system
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Simulated Annealing Scheduling on Kubernetes Clusters: An E-Health Digital Twin Use-Case
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Real-Time Node’s Power-Aware Kubernetes Scheduler in a Cloud Environment
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Virtualization and Cloud Computing
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A Deep Learning Systematic Literature Review for Resource Usage Prediction and Automatic Scaling in Kubernetes Clusters
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Comparative Study Between Popular Container Orchestrators: Kubernetes, K3s, and Docker Swarm
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LR2Scheduler: layer-aware, resource-balanced, and request-adaptive container scheduling for edge computing
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Enhancing the Kubernetes Platform with a Load-Aware Orchestration Strategy
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Load-balanced scheduling optimization strategy for high-communication tasks in Kubernetes with RDMA
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Heterogeneous edge AI orchestration: A systematic review, taxonomy and future directions
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Application type awareness pod-level and system-level container scheduling
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Enhancing fog IoT container deployment: A customizable Kubernetes scheduler
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NGS: A network GPGPU system for orchestrating remote and virtual accelerators
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AROF: adaptive resource optimization framework for Kubernetes cluster using workload forecasting
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Dynamic, Context-Aware Cross-Layer Orchestration of Containerized Applications
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A Framework for Cognitive, Decentralized Container Orchestration
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KubeAI-An AI-Powered Pod Placement Scheduler for Kubernetes
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Towards Monitoring-Driven and Workload-Aware Scheduling for Federated Learning in Cloud-Edge Environments
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3GC: A Deadline-Aware and Energy-Efficient Resource Allocation Scheme for Serverless Edge Computing
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Application and Infrastructure-Aware Orchestration in the Cloud-to-Edge Continuum
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Telemetry-Driven Microservices Orchestration in Cloud-Edge Environments
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Dynamic In-node Group-Aware Scheduling for Multi-Tenant Machine Learning Services on Kubernetes
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SLO-Aware Container Orchestration on Kubernetes Clusters
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Two-level Network Bandwidth Allocation for Multi-tenant AI Clouds in Kubernetes
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Evaluating Kubernetes Distributions: Insights from Stress Testing Scenarios
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Multi Criteria Based Container Management in a Geo-Distributed Cluster
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Reliability Engineering Metrics in Continuous Deployment
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Kubernetes Scheduling Design Based on Imitation Learning in Edge Cloud Scenarios
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