eBPF-Accelerated Cloud Networking and Kernel-Bypass Service Mesh for High-Throughput AI Clusters

Distributed artificial intelligence training, disaggregated retrieval-augmented generation (RAG) inference, and high-frequency cloud-native microservices place unprecedented bandwidth and tail-latency demands on Kubernetes cluster networking. Conventional Kubernetes Container Network Interfaces (CNIs) and sidecar-based service meshes rely on chained `iptables` netfilter rules and per-pod user-space Envoy proxies that traverse the Linux kernel TCP/IP stack up to six times for a single pod-to-pod RPC. Under high-concurrency AI tensor streaming and gRPC microservice fan-out, this architecture induces severe context-switching overhead, L1/L2 instruction cache pollution, ephemeral port exhaustion, and tail-latency spikes exceeding 15 milliseconds at the 99th percentile. This paper presents an eBPF-accelerated cloud networking and kernel-bypass service mesh architecture designed for multi-tenant AI and microservice clusters. The architecture integrates eXpress Data Path (XDP) driver-level packet processing, `tcx` (traffic control express) eBPF host routing, socket-level `BPF_MAP_TYPE_SOCKHASH` loopback splicing (`sk_msg`), Big TCP (up to 192 KB GSO/GRO super-packets), and a sidecarless per-node L7/mTLS datapath modeled on Cilium and Hubble. We formulate a mathematical queueing and CPU-cycle cost model comparing `iptables` + sidecar traversal against eBPF socket redirection and ECN-assisted congestion pacing. We evaluate the architecture using production engineering case studies from Datadog and Adobe multi-cluster Kubernetes environments alongside controlled benchmarks on 100 Gbps and 400 Gbps cloud nodes running disaggregated vLLM inference and distributed KV-cache synchronization. Empirical results show that the eBPF datapath reduces p99 RPC latency by 64.8% (from 4.82 ms to 1.70 ms), elevates sustained node-to-node throughput from 58.4 Gbps to 94.6 Gbps per 100GbE link, cuts networking vCPU consumption by 57.3%, and scales linearly across 50,000+ Kubernetes services with $O(1)$ hash lookup complexity. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Cloud Networking, Linux Kernel Datapaths, and Kubernetes Service Mesh. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.