# MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling

arXiv: https://arxiv.org/abs/2603.07607 · HTML: https://mlsanity.com/paper/2603.07607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 79 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling](https://mlsanity.com/paper/2507.07932/) (2025)
- [ABACUS: A FinOps Service for Cloud Cost Optimization](https://mlsanity.com/paper/2501.14753/) (2024)
- [Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN](https://mlsanity.com/paper/2209.02551/) (2022)
- [Distributed Optimal Steady-State Regulation for High-Order Multi-Agent Systems with External Disturbances](https://mlsanity.com/paper/1902.00865/) (2019)
- [Proactive Autoscaling for Edge Computing Systems with Kubernetes](https://mlsanity.com/paper/2112.10127/) (2021)
- [Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach](https://mlsanity.com/paper/2412.02146/) (2024)
- [ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments](https://mlsanity.com/paper/2505.02964/) (2025)
- [Streamlining Resilient Kubernetes Autoscaling with Multi-Agent Systems via an Automated Online Design Framework](https://mlsanity.com/paper/2505.21559/) (2025)

## Abstract (arXiv)

Autoscaling in cloud-native platforms like Kubernetes is reactive and metric-driven, leading to a strategic void problem. This comes from the decoupling of higher-level business policies from lower-level resource provisioning. The strategic void, coupled with a fragmented coordination of pod and node scaling, can lead to significant resource waste and performance degradation under dynamic workloads. In this paper, we present MAS-H2, a new hierarchical multi-agent system that addresses the challenges of autonomic cloud resource management with a complete end-to-end solution. MAS-H2 systematically decomposes the control problem into three layers: a Strategic Agent that formalises business policies (e.g., cost vs. performance) into a global utility function; Planning Agents that produce a joint, proactive scaling plan for pods and nodes with time-series forecasting; and Execution Agents that execute the scaling plan. We built and tested a MAS-H2 prototype as a Kubernetes Operator on Google Kubernetes Engine (GKE) to benchmark it against the native Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler (CA) baselines under two realistic, spiky, and stress-inducing workload scenarios. The results show that the MAS-H2 system maintained application CPU usage under 40% for predictable Heartbeat workloads. This resulted in over 50% less sustained CPU stress than the native HPA baseline, which typically operated above 80%. The MAS-H2 system demonstrated proactive planning in a volatile Chaotic Flash Sale scenario by filtering transient noise and deploying more replicas compared to HPA. It reduced peak CPU load by 55% without under-provisioning. Beyond performance, MAS-H2 seamlessly performed a zero-downtime strategic migration between two cost- and performance-optimised infrastructures.