---
title: 'ProxFly: Unikernel TCP & RL Quadcopter Control'
url: https://www.emergentmind.com/topics/proxfly
type: topic
---

# ProxFly: Unikernel TCP & RL Quadcopter Control

ProxFly encompasses two distinct, high-efficiency systems in network and robotics research: (1) a unikernel-based, on-the-fly TCP acceleration architecture built on Miniproxy and (2) a robust quadcopter control framework leveraging residual reinforcement learning for close-proximity flight. Each system represents state-of-the-art approaches within its domain, offering significant performance improvements, resource efficiency, and rigorous experimental validation [1605.06285][2409.13193].

## 1. Unikernel-Based ProxFly for On-the-Fly TCP Acceleration

### Architecture and Cloud OS Integration

ProxFly’s TCP acceleration architecture is implemented using Miniproxy, a Xen unikernel based on MiniOS and a patched lwIP stack. MiniOS provides a paravirtualized, single-address-space kernel with no traditional system calls and achieves boot times on the order of tens of milliseconds. The entire Miniproxy VM is a static ELF image requiring only ~6 MB RAM, supporting massive consolidation. At boot, Miniproxy immediately launches a packet RX/TX loop (via the Xen network backend) and a TCP proxy application managing each proxied connection as a pair of lwIP protocol control blocks (PCBs). A 12-byte custom TCP option enables explicit proxy chaining by embedding the client–server 4-tuple in the SYN, enabling Early SYN Forwarding even for explicit proxies [1605.06285].

### TCP Connection Handling Optimizations

ProxFly fundamentally restructures TCP handshake and slow-start timing via the following mechanisms:

- **Split-TCP and Naïve Proxy Chaining**: Without proxies, the 3-way handshake plus first data incurs latency of $4D$ ($D$ being the one-way delay). Sequential proxy handshakes do not improve this.
- **Early SYN Forwarding (ESF)**: On SYN reception, the proxy immediately forwards the SYN, causing the client–proxy and proxy–server handshakes to overlap. For delays $X_1$ and $X_2$ (such that $X_1 + X_2 = D$):
  $$
  TTFB_{ESF} = 2D + 2\max(X_1, X_2)
  $$
  In the balanced configuration ($X_1 = X_2 = D/2$), this yields a $25\%$ TTFB reduction ($3D$). For $N$ evenly spaced in-path proxies:
  $$
  TTFB_{ESF} = 2D(1 + \frac{1}{N+1})
  $$
- **Slow Start Acceleration**: Parallelization via proxy splitting reduces effective RTT during window ramp-up. For $k$ slow-start slots and $N$ proxies:
  $$
  TTC_{split} = 2D + 2\frac{D}{N+1} + 2k\frac{D}{N+1}
  $$

### Mathematical Model: Boot Time, Resources, and Latency Tradeoffs

Letting $t_{boot}$ denote proxy instantiation time, $M$ RAM per instance, and $C$ the CPU cycles per packet, the end-to-end time to first byte with on-the-fly instantiation is:
$$
TTFB_{fly} = t_{boot} + 2D\left(1 + \frac{1}{N+1}\right)
$$
The improvement over the baseline (no proxy) is:
$$
\Delta TTFB = 2D\left(1 - \frac{1}{N+1}\right) - t_{boot}
$$
For transfer completion after $k$ RTT slots:
$$
TTC_{fly} = t_{boot} + 2D + 2(k+1)\frac{D}{N+1}
$$
$$
\Delta TTC = 2(k+1)D\left(1 - \frac{1}{N+1}\right) - t_{boot}
$$
A single host with $R$ MB RAM can run $R/M$ Miniproxy instances (e.g., $64$ GB RAM yields $\sim$10,000 proxies at $6$ MB each, vs. $64$ Linux proxies at $1$ GB each).

### Quantitative Results

In empirical evaluation, Miniproxy achieved 1.534 Gb/s throughput (vs. Varnish’s 1.462 Gb/s), with $\sim$5% higher throughput and two orders of magnitude less memory usage. Boot times at $2$–$3$ GHz CPUs are $\sim$12 ms for $6$ MB RAM; up to $8$ MB RAM produces $t_{boot} < 60$ ms ($<230$ ms at $800$ MHz). Median per-flow SYN processing is under $3$ ms for up to 230 concurrent connections. For a 100 ms RTT path and 10 KB flow, adding proxies reduces transfer time from $400$ ms (no proxy) to $300$ ms (1 proxy), $266$ ms (2), or $250$ ms (3) – up to $37.5\%$ improvement. For 25 KB slow-start-dominated transfers, savings rise to $33$–$49\%$ [1605.06285].

### Deployment Modes and Operational Guidelines

Principal modes include:

- **Edge cloud acceleration**: Proxies instantiate in under $50$ ms near the client.
- **Massive isolation**: $\sim$$10^4$ Miniproxy instances per $64$ GB RAM for per-flow/tenant separation.
- **Just-in-time provisioning**: Boot on-demand using orchestration triggered by SYN packet observation; balance placement for maximum latency gain.

Guidelines include memory tuning (6–8 MB per instance for $\sim$$10^4$ flows), CPU provisioning ($>1$ Gb/s per 3 GHz core), security reinforcement (unikernel surface minimization; authenticate explicit proxy SYN options), rate-limited instantiation to prevent storm effects, and careful path selection to avoid offsetting gains with path-length increases.

## 2. Residual RL-Based ProxFly for Close Proximity Quadcopter Control

### Quadcopter Dynamics and Baseline Control

The control framework models standard Newton–Euler rigid-body dynamics in world-frame position $p\in\mathbb{R}^3$ and body-frame angular velocity $\omega\in\mathbb{R}^3$:
$$
m\,\ddot p = m g e_3 + R(\phi,\theta,\psi)\,T + d_{\rm ext},
$$
$$
I\,\dot\omega + \omega \times (I\,\omega) = \tau + \tau_{\rm ext}
$$
with $m$ mass, $R$ rotation matrix, $T$ thrust, $\tau$ torque, and $d_{\rm ext}, \tau_{\rm ext}$ representing disturbances (notably, aerodynamic downwash in close-proximity flight).

The cascaded controller consists of:

- **Outer loop (position ⟶ thrust/attitude)**: Given position/velocity errors, applies PD-style law to output normalized thrust $c_{\rm cas}$ and desired attitude $R_{\rm des}$.
- **Inner loop (attitude ⟶ body rates)**: Computes attitude error and outputs compensation body rates $\omega_{\rm cas}$.
- **Basic command vector**: $u_{\rm basic} = [c_{\rm cas}, \omega_{\rm cas}]^T \in \mathbb{R}^4$.

### Residual RL Module

ProxFly introduces a residual policy on top of the model-based controller, with the following structure:

- **Observation space** ($20$-dim): Includes current error states, last action, and basic controller output.
- **Action**: Residual on thrust and rates, $u_{\rm res} \in \mathbb{R}^4$, with clipping to practical actuation bounds.
- **Reward**: Weighted sum of position offset, attitude deviation, thrust/rate penalties, and a survival bonus.
- **Final command**: $u_t = u_{\rm basic}(s_t) + u_{\rm res}(o_t)$.

The actor (policy) and critic (value) networks are both 3-layer MLPs (128 units/layer, LeakyReLU, $\tanh$ output). Training uses Proximal Policy Optimization (PPO) with advantage estimation, clipped surrogate objective, and Adam optimization [2409.13193].

### Domain Randomization and Robustness Strategy

To enforce robustness and rapid adaptation, every episode randomizes:

- Mass and inertia (per-episode, up to $\pm50\%$ variance)
- Propeller constants (per-motor)
- External vertical/horizontal disturbance profiles (triangular waves; amplitude $0.25$–$4$ N)
- Additive torque noise (Gaussian)

This broad parameter sweep forces the residual policy to generalize across identification errors and uncertain/unmodeled turbulence.

### Experimental Validation

**Simulation**: Two-vehicle scenarios with high-fidelity downwash (Karana et al. model) test at separations $0.25$–$0.75$ m, measuring altitude error, attitude RMSE, and control residuals. The RL-added residuals correct for steady-state errors induced by downwash, despite never having seen the Karana model in training.

**Real-World Experiments**: Using precision motion capture and multi-rate control loops (high-level $50$ Hz, low-level $500$ Hz), three baselines are compared: the basic cascaded controller, a finely tuned model-based downwash compensator (FB-AeroComp), and ProxFly. Metrics include position RMSE $E_{\rm pos}$ and attitude RMSE $E_{\rm att}$:

| Task           | Basic           | FB-AeroComp     | ProxFly         |
|----------------|-----------------|-----------------|-----------------|
| Hovering       | 0.1199 m, 0.1710 rad | 0.1113 m, 0.1818 rad | 0.0882 m, 0.0794 rad |
| Circling (same)| 0.1867 m, 0.1976 rad | 0.0832 m, 0.1238 rad | 0.1385 m, 0.1252 rad |
| Circling (rev) | 0.1451 m, 0.1714 rad | 0.0983 m, 0.0930 rad | 0.0940 m, 0.0996 rad |
| **Average**    | 0.1506 m, 0.1800 rad | 0.0976 m, 0.1329 rad | 0.1069 m, 0.1014 rad |

ProxFly reduces mean position error (vs. basic) by approximately $29\%$, and mean attitude error by $44\%$, while matching or outperforming FB-AeroComp in hover.

**In-Air Docking**: The system handles rapid load/inertia change and strong turbulence when a small quadcopter drops $5$ cm onto a larger hovering vehicle without loss of stability, the residual thrust response peaks and then returns to nominal.

### Discussion, Limitations, and Prospects

By learning only the residual atop a validated controller, ProxFly achieves interpretable, sample-efficient policy refinement with reduced “black box” exposure and avoids the need for inter-vehicle communication. However, high-frequency oscillations in the residual output may stress hardware; extreme parameter mismatch remains a possible failure point; and broader physical generalization awaits further demonstration. Possible extensions include online residual smoothing (system ID), integration of perceptual sensing, and multi-agent explicit intent sharing [2409.13193].

## 3. Broader Significance and Impact

ProxFly, across domains, exemplifies minimalist, high-performance system design:

- In networking, it demonstrates that high-frequency, on-the-fly instantiation of TCP-accelerating proxies is practical and efficient, even at per-flow granularity.
- In aerial robotics, it establishes a pathway whereby model-based control augmented by RL-trained residuals yields robust, communication-minimal, and hardware-constrained high-agility behaviors previously only achievable via purpose-built, model-tuned controllers.

Both systems explicitly target real-world deployment scenarios: edge-cloud TCP orchestration and safe, reliable close-proximity quadcopter maneuvers, including aggressive actions such as mid-air docking.

## 4. Trade-offs, Operational Constraints, and Deployment Considerations

- Resource provisioning: In ProxFly-TCP, small RAM and fast boot times permit massive scalability and per-tenant isolation; in ProxFly-RL, computation and memory are dictated by actor-critic MLP inference rates and sensor/control latency budgets.
- Security: Unikernel architecture in Miniproxy naturally reduces exploit surface; ProxFly quadrotor system can install further safety assurance layers, though the RL residual could in principle produce unexpected time-series artifacts.
- Latency/throughput scalability: In networks, orchestration operations must not offset handshake/slow-start gains; in RL flight, the controller must maintain closed-loop frequency under typical wireless/CPU burdens.
- Limitations: For both, edge cases (mini-flows in TCP, extreme mass/turbulence in drones) may undercut net benefits, and orchestration or parameter tuning complexity may grow with deployment scale.

## 5. Summary

ProxFly, as instantiated in both TCP acceleration and quadcopter control, represents minimalist, resource-efficient high-performance solutions, validated in both empirical and theoretical analysis. Key elements are (1) rapid, lightweight proxy instantiation for fine-grained, on-demand TCP acceleration with verified resource and latency savings [1605.06285], and (2) residual RL augmentation of interpretable controllers, yielding robust close-proximity flight with performance matching specialized model-based compensators—without the need for inter-agent communication [2409.13193].

For further technical and implementation details, consult the original references and supporting codebase [1605.06285][2409.13193].

Source: https://www.emergentmind.com/topics/proxfly