[Paper] KUBEDIRECT: Unleashing the Full Power of the Cluster Manager for Serverless Computing

Source: arXiv - 2601.19160v1

Overview

FaaS platforms rely on cluster managers like Kubernetes for resource management. Kubernetes is popular due to its state‑centric APIs that decouple the control plane into modular controllers. However, to scale out a burst of FaaS instances, message passing becomes the primary bottleneck as controllers have to exchange extensive state through the API Server. Existing solutions opt for a clean‑slate redesign of cluster managers, but at the expense of compatibility with the existing ecosystem and substantial engineering effort.

We present KUBEDIRECT, a Kubernetes‑based cluster manager for FaaS. We find that there exists a common narrow waist across FaaS platforms that allows us to achieve both efficiency and external compatibility. Our insight is that the sequential structure of the narrow waist obviates the need for a single source of truth, allowing us to bypass the API Server and perform direct message passing for efficiency. However, this approach introduces a set of ephemeral states across controllers, making it challenging to enforce end‑to‑end semantics due to the absence of centralized coordination.

KUBEDIRECT employs a novel state‑management scheme that leverages the narrow waist as a hierarchical write‑back cache, ensuring consistency and convergence to the desired state. KUBEDIRECT can seamlessly integrate with Kubernetes, adding ~150 LoC per controller. Experiments show that KUBEDIRECT reduces serving latency by 26.7× over Knative and achieves performance comparable to the state‑of‑the‑art clean‑slate platform Dirigent.

Key Contributions

• Introduces KUBEDIRECT, a Kubernetes‑compatible cluster manager for FaaS.
• Demonstrates a narrow‑waist‑based hierarchical write‑back cache for state consistency.
• Shows a 26.7× latency reduction over Knative with minimal code additions.
• Provides compatibility with existing Kubernetes ecosystems while matching clean‑slate performance.

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research advances the field of distributed computing (cs.DC) by offering a practical, low‑overhead solution for scaling FaaS workloads on Kubernetes.

Authors

• Sheng Qi
• Zhiquan Zhang
• Xuanzhe Liu
• Xin Jin

Paper Information

• arXiv ID: 2601.19160v1
• Categories: cs.DC
• Published: January 27, 2026
• PDF: Download PDF