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arXiv · 2609.38235

Characterizing the eBPF-Based Data Plane for Multi-ClusterKubernetes: A Systematic Evaluation of Cilium Cluster Meshand KVStoreMesh

Simhadri Podala Narasimha
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Abstract

Kubernetes deployments increasingly span multiple clusters for reasons of scale, fault isolation, regulatory boundaries, and heterogeneous hardware placement, including GPU-dense clusters dedicated to AI and HPC workloads. Cilium Cluster Mesh, built on extended Berkeley Packet Filter (eBPF) technology, has emerged as a widely deployed mechanism for connecting such clusters into a single logical network without a dedicated multi-cluster gateway. Despite its adoption, the academic literature contains no systematic, reproducible evaluation of the eBPF-based multi-cluster data plane itself: existing work either benchmarks eBPF against iptables within a single cluster, or applies eBPF to cross-cluster monitoring rather than forwarding and identity propagation. The most detailed scale data available - a report of a clustermesh-apiserver failing under load at approximately 45,000 nodes across 256 clusters - comes from an industry engineering blog rather than a peer-reviewed source. This paper proposes a methodology to close that gap. We define five research questions covering latency, throughput, control-plane limits, churn, and GPU/RDMA sensitivity, and describe a testbed and eBPF-level instrumentation design. Lacking a live multi-cluster testbed for this submission, we report results from a calibrated analytical simulation of control-plane architectures fit to the production data point. These results are presented as falsifiable predictions intended for validation once the testbed is complete. This paper provides the motivation, experimental design, and preliminary simulation results to serve as a template for a full empirical study.

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Simhadri Podala Narasimha. 2026-09-28. Characterizing the eBPF-Based Data Plane for Multi-ClusterKubernetes: A Systematic Evaluation of Cilium Cluster Meshand KVStoreMesh. https://doi.org/10.48550/arxiv.2609.38235

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