chore(brain): ASTRA 성장 자산 동기화 — 기능 인벤토리·growth(약점프로필/학습큐)·일화기억·장기기억·회의록 원문

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+---
+id: wiki-2026-0508-edge-computing
+title: Edge Computing
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Edge AI, Fog Computing, Edge Inference, MEC]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.92
+verification_status: applied
+tags: [edge, iot, distributed-systems, latency, on-device]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Rust/C++/Python
+  framework: K3s / Cloudflare Workers / ONNX Runtime / WasmEdge
+---
+
+# Edge Computing
+
+## 매 한 줄
+> **"매 edge computing 의 핵심: compute moves to data — latency + bandwidth + privacy"**. 매 2010s CDN edge → 매 2020s function-edge (Cloudflare Workers, Deno Deploy, AWS Lambda@Edge), 매 2022 edge AI inference, 매 2024 5G MEC. 매 2026 현재 phone (Apple Intelligence, Pixel Gemini Nano) → CDN edge (Workers AI) → micro-DC 의 3-tier edge stack 의 일반화.
+
+## 매 핵심
+
+### 매 edge spectrum
+- **Device edge**: 매 phone, sensor, MCU, robot.
+- **Near edge / On-prem**: 매 factory floor, retail store K8s.
+- **Far edge / CDN**: 매 Cloudflare, Fastly, Akamai PoPs.
+- **MEC (Mobile Edge Computing)**: 매 5G base station co-located.
+
+### 매 drivers
+- **Latency**: 매 <10ms — 매 cloud round-trip 의 불가능.
+- **Bandwidth**: 매 video / sensor stream의 cloud 전송 cost.
+- **Privacy / sovereignty**: 매 data 의 region / device 외 미반출.
+- **Resilience**: 매 offline operation.
+- **Cost**: 매 egress fee 회피.
+
+### 매 응용
+1. Real-time vision (autonomous driving, AR).
+2. Voice assistants (Siri on-device wake-word).
+3. Industrial control (PLC + edge AI).
+4. Game streaming, low-latency RTC.
+5. Edge AI inference (Llama 3.2 on phone, Whisper on Pi).
+
+## 💻 패턴
+
+### Cloudflare Workers (function edge)
+```ts
+export default {
+  async fetch(req: Request, env: Env): Promise<Response> {
+    const cache = caches.default;
+    const cached = await cache.match(req);
+    if (cached) return cached;
+    const data = await env.DB.prepare("SELECT * FROM posts LIMIT 10").all();
+    const res = new Response(JSON.stringify(data),
+      { headers: { "cache-control": "max-age=60" } });
+    await cache.put(req, res.clone());
+    return res;
+  }
+};
+```
+
+### Workers AI (edge LLM inference)
+```ts
+export default {
+  async fetch(req, env) {
+    const { prompt } = await req.json();
+    const out = await env.AI.run("@cf/meta/llama-3.2-3b-instruct",
+      { prompt, max_tokens: 200 });
+    return Response.json(out);
+  }
+};
+```
+
+### K3s (lightweight K8s for edge)
+```bash
+# Install on edge node
+curl -sfL https://get.k3s.io | sh -s - --disable traefik --node-name edge-01
+# Deploy edge inference service
+kubectl apply -f - <<EOF
+apiVersion: apps/v1
+kind: Deployment
+metadata: { name: edge-yolo }
+spec:
+  replicas: 1
+  template:
+    spec:
+      nodeSelector: { tier: edge }
+      containers:
+      - name: yolo
+        image: ghcr.io/me/yolo11n:latest
+        resources: { limits: { cpu: "2", memory: 2Gi } }
+EOF
+```
+
+### ONNX Runtime on edge (Raspberry Pi 5)
+```python
+import onnxruntime as ort
+import numpy as np
+sess = ort.InferenceSession("yolov11n.onnx",
+    providers=["CPUExecutionProvider"])
+out = sess.run(None, {"images": img.astype(np.float32)})
+```
+
+### WasmEdge (portable edge runtime)
+```rust
+// Compile Rust → wasm32-wasi
+fn main() {
+    let img = std::fs::read("input.jpg").unwrap();
+    let result = run_inference(&img);
+    println!("{:?}", result);
+}
+// Run: wasmedge app.wasm
+```
+
+### MQTT-bridged hierarchical edge
+```python
+# Edge gateway aggregates sensors, forwards summaries to cloud
+import paho.mqtt.client as mqtt
+
+def on_local(client, _, msg):
+    summary = aggregate(msg.payload)
+    cloud.publish("plant/summary", summary)
+
+local = mqtt.Client(); local.connect("localhost"); local.on_message = on_local
+local.subscribe("sensors/#"); local.loop_start()
+
+cloud = mqtt.Client(); cloud.connect("cloud.broker.com")
+```
+
+### CRDT-based offline-first sync
+```ts
+import * as Y from "yjs";
+import { WebsocketProvider } from "y-websocket";
+const doc = new Y.Doc();
+// Works offline, merges automatically when reconnected
+new WebsocketProvider("wss://sync.edge.local", "doc1", doc);
+```
+
+### Edge LLM with quantization (mobile)
+```python
+# Convert Llama 3.2 3B to 4-bit GGUF for mobile
+# llama.cpp or MLX
+from mlx_lm import convert
+convert("meta-llama/Llama-3.2-3B-Instruct",
+        mlx_path="llama-3b-q4", quantize=True, q_bits=4)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Static asset / API cache | CDN (Cloudflare, Fastly) |
+| Low-latency API | Workers / Deno Deploy |
+| Stateful edge service | K3s on near-edge |
+| Sensor / IoT | MQTT + edge gateway |
+| Edge AI inference | ONNX Runtime / TFLite / Core ML |
+| Cross-platform portable | Wasm (WasmEdge, Spin) |
+| Personal AI | On-device LLM (MLX, llama.cpp) |
+
+**기본값**: 매 latency-sensitive read 은 CDN edge, 매 sensor data 의 edge gateway 에서 aggregate.
+
+## 🔗 Graph
+- 부모: [[Distributed Systems]]
+- 변형: [[CDN]] · [[Serverless]] · [[MEC]]
+- 응용: [[클라우드_인프라_및_IaC_운영_표준|IoT]] · [[Edge AI]] · [[On-device AI]]
+- Adjacent: [[Cloud Native]] · [[Data Privacy & Local Processing]]
+
+## 🤖 LLM 활용
+**언제**: 매 edge deployment scaffolding (K3s manifests, Workers code), 매 quantization workflow, 매 latency-budget reasoning.
+**언제 X**: 매 hard-real-time (<1ms) — 매 LLM 의지 X. 매 deterministic timing 의 RTOS 사용.
+
+## ❌ 안티패턴
+- **Edge as cloud-with-extra-steps**: 매 unnecessary 사용 — 매 latency / privacy 명확한 driver 없으면 cloud.
+- **No degradation strategy**: 매 edge offline → 전체 fail.
+- **State at every edge**: 매 consistency nightmare — 매 CRDT / eventual 의 사용.
+- **Same model size as cloud**: 매 device OOM — 매 quantize + distill.
+- **Ignoring network partitions**: 매 split-brain → corrupted state.
+- **Pushing all logic to client**: 매 trust boundary violation.
+
+## 🧪 검증 / 중복
+- Verified (Cloudflare Workers docs 2025, K3s docs, ETSI MEC spec, ONNX Runtime mobile docs, Llama 3.2 release notes 2024).
+- 신뢰도 A.
+- 관련: [[Cloud Native]], [[Data Privacy & Local Processing]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — edge spectrum + 2026 tooling (Workers AI, MLX, K3s) |