# Mec Offloading Eval

> Evaluates the stability, convergence, and resource efficiency of online computation offloading algorithms in dynamic mobile-edge networks under stochastic task arrivals and time-varying channel conditions. It probes whether an algorithm can maintain queue stability and power constraints while maximizing computation throughput. Use when the user wants to benchmark on Simulated MEC Offloading Environment, or asks about evaluating this task. Reports weighted sum computation rate.

- Skill: `qhjqhj00/mec-offloading-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/mec-offloading-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/mec-offloading-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/mec-offloading-eval

---


# mec-offloading-eval

> Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks — Bi et al. (2020) (arXiv:2010.01370, 2020)

## What this evaluates

Evaluates the stability, convergence, and resource efficiency of online computation offloading algorithms in dynamic mobile-edge networks under stochastic task arrivals and time-varying channel conditions. It probes whether an algorithm can maintain queue stability and power constraints while maximizing computation throughput.

## Datasets

- **Simulated MEC Offloading Environment** — total ?; splits: test (-1); repo https://github.com/revenol/LyDROO

## Metrics

- `weighted sum computation rate` **(primary)** — range: other
  - Sum of per-device computation rates weighted by coefficients c_i: \sum_{i=1}^N c_i r_i^t. Measured in Mbps.
- `average data queue length` — range: other
  - Time-averaged backlog of task data at each wireless device, indicating stability. Stable if bounded over time.
- `average power consumption` — range: other
  - Time-averaged energy consumption per device, constrained by threshold \gamma_i.
- `optimality ratio` — range: [0, 1]
  - Ratio of LyDROO's per-frame objective value to the LyCD benchmark's objective value for subproblem (P2).

## Input / output format

**Input**: State vector \xi^t containing channel gains, data queue backlogs, energy queue states, and task arrival rates for N wireless devices at each time frame t.

**Output**: Mixed-integer-continuous offloading action vector: binary offloading decisions x_i^t, transmission time \tau_i^t, local computation frequency f_i^t, and offloaded energy e_{i,O}^t for each device i.

## Scoring recipe

```python
# Initialize queues and accumulate metrics over T frames
for t in range(T):
    # Compute per-device computation rate r_i^t from action
    weighted_rate = sum(c_i * r_i^t for i in range(N))
    # Update data queue: Q_i^{t+1} = max(Q_i^t + A_i^t - r_i^t, 0)
    # Update energy queue: E_i^{t+1} = max(E_i^t + \gamma_i - e_i^t, 0)
    # Accumulate averages
avg_queue_len = mean(Q_i^t for t in range(T))
avg_power = mean(e_i^t for t in range(T))
optimality_ratio = objective_LyDROO / objective_LyCD
```

## Common pitfalls

- Confusing short-term rate spikes with long-term stability; queue stability requires bounded backlog over thousands of frames, not just high instantaneous rate.
- Ignoring the power constraint violation; algorithms may achieve high rates but violate the average power threshold \gamma_i, which invalidates their feasibility.
- Misinterpreting the Lyapunov parameter V; larger V improves rate but increases queue length and power consumption, requiring careful tuning for practical deployment.

## Evidence (verbatim from paper)

> In Fig.[5](#S6.F5 "Equation 51c ‣ Equation 51 ‣ 2nd item ‣ VI Simulation Results ‣ Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks"), we consider two data arrival rates with $\lambda_{i}\=2.5$ and $3$ Mbps for all $i$, and plot the weighted sum computation rate, average data queue length, and average power consumption performance over time.

## Citation

```bibtex
@misc{bi2020lydroo,
  title={Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks},
  author={Bi et al. (2020)},
  year={2020},
  note={arXiv:2010.01370}
}
```

- arXiv: 2010.01370