LQR-Based CPU Traffic Regulation for Multi-Tenant Open vSwitch with XDP Actuation

Multi-tenant Open vSwitch (OVS) deployments share a single datapath process and a common pool of central processing unit (CPU) cycles, creating risk of cross-tenant performance interference when sustained tenant traffic surges exhaust shared CPU cycles. We present a closed-loop optimal regulator that enforces per-tenant CPU budgets at the network interface. A two-state Linear-Quadratic Regulator (LQR), tracking CPU error and its backward-difference rate, is synthesized from an offline-identified first-order plant model and actuated via driver-level packet dropping within a single shared extended Berkeley Packet Filter (eBPF)/eXpress Data Path (XDP) program with per-namespace eBPF maps. The control loop executes in user space every 0.5 s; per-packet drop decisions execute in the driver at roughly 15–20 eBPF bytecode instructions. Evaluated on a gigabit testbed with single and dual OVS namespaces handling 512-byte User Datagram Protocol (UDP) traffic, the controller demonstrates tight set-point tracking: under active load (≥300 Mbps), single-namespace conditional mean tracking errors, in percentage points (pp), are +0.25 (3% target), +1.32 (4% target), and +0.18 (5% target); a dual-namespace pair holds the combined target within 1.9 pp with near-perfect fairness (J=0.9964). The three targets expose a clear throughput–CPU trade-off: the tight 3% budget is enforced by dropping roughly 60–63% of offered packets in total (commanded drops plus residual buffer loss), whereas the 5% budget delivers the highest throughput efficiency (up to 50.5% in dual-tenant operation). During a 100 Mbps → 500 Mbps step disturbance at a 5% CPU target, the controller peaks at 8.65% (+3.65 pp overshoot), with the initial transient resolving within ts≈2.8 s; no growing control-loop oscillations are observed. Against a single-point-tuned proportional–integral–derivative (PID) baseline at the 5% budget, LQR exhibits descriptively lower conditional mean tracking error (+0.18 pp vs. +0.77 pp, absolute difference of 0.59 pp; single-run point estimate, n=1, not statistically validated). Under 500 Mbps dual-tenant load, regulation saves 28.6% of host CPU cycles at a 4.1 pp throughput efficiency cost. The multi-tenant evaluation uses N=2 namespaces; control-law and datapath scalability to larger N is argued analytically by the O(1) per-tenant complexity, but physical-plant separability for N>2 requires separate empirical validation. The reference implementation, evaluation harness, and analysis scripts are publicly available.