Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling

[2603.12031] Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling Skip to main content Search Submit Donate Log in Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.12031 (cs) [Submitted on 12 Mar 2026 ( v1 ), last revised 28 Aug 2026 (this version, v4)] Title: Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling Authors: Hamed Hamzeh View PDF HTML (experimental) Abstract: Cloud-native container orchestration requires resource schedulers capable of balancing infrastructure expenditure, fault resilience, and node utilisation. Conventional reinforcement learning approaches typically rely on monolithic single-agent models that suffer from gradient interference and reward dilution when mapping conflicting operational goals into a single scalar reward. We present Agentic-Kube, a cooperative multi-agent reinforcement learning framework designed for real-time Kubernetes pod placement. The architecture decomposes multi-objective scheduling into a tripartite optimisation space managed by dedicated sub-agents for cost minimisation, anti-affinity fault tolerance, and vector resource balancing. Agentic-Kube integrates a bipartite Graph Convolutional Network to capture dynamic host-pod dependencies, a two-stage monotonic QMIX value factorisation network to maintain joint action value coherence, and a plurality voting consensus mechanism with action feasibility masking against allocatable node predicates. We evaluate the framework across live heterogeneous Google Kubernetes Engine deployments and macro-scale cluster environments spanning 50 to 1,000 nodes under empirical Alibaba trace data, diurnal microservice variations, and flash-crowd bursts. Across physical and simulated evaluations, Agentic-Kube consistently achieves Pareto-efficient placements. In diurnal microservice workloads, it reduces anti-affinity service collisions to 7.11%, representing a 53.0% relative reduction compared to the default Kubernetes scheduler. Under Alibaba traces, the policy achieves a 65.15% spot instance allocation ratio, while macro-scale benchmarks demonstrate scaling up to 1,000 nodes with mean decision latencies under 17ms and 99th-percentile latencies under 31ms, executing without container restart failures and operating well within standard scheduling admission timeouts. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2603.12031 [cs.DC] (or arXiv:2603.12031v4 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2603.12031 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hamed Hamzeh [ view email ] [v1] Thu, 12 Mar 2026 15:09:48 UTC (3,661 KB) [v2] Thu, 7 May 2026 16:33:01 UTC (3,647 KB) [v3] Fri, 21 Aug 2026 20:46:42 UTC (23,519 KB) [v4] Fri, 28 Aug 2026 10:09:18 UTC (23,519 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2026-03 Change to browse by: cs cs.LG cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling#682607
• MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning#353046
• Multi-objective application placement in fog computing using graph neural network-based reinforcement learning#187283
• Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning#303228
• Graph-based Hierarchical Deep Reinforcement Learning for Deliverable Block Propagation with Optimal Hybrid Cost in Web 3.0#31236