# 用 wasmedge 直接运行一个 wasm 模块并测冷启动
time wasmedge --dir .:/ hello.wasm
# 输出 real ~ 0.02s，远低于容器启动

# 在 K8s 中通过 RuntimeClass 声明 wasm 运行时
cat > wasm-runtimeclass.yaml <<'EOF'
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: wasmedge
handler: wasmedge          # containerd 中注册的 wasm handler
EOF
kubectl apply -f wasm-runtimeclass.yaml

# containerd 配置 /etc/containerd/config.toml
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.wasm]
  runtime_type = "io.containerd.wasmedge.v1"   # WASM 运行时
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc]
  runtime_type = "io.containerd.runc.v2"      # 普通 Linux 容器

# 同一集群内两类负载混部
apiVersion: v1
kind: Pod
metadata:
  name: wasm-fn
spec:
  runtimeClassName: wasmedge      # 走 WASM
  containers:
    - name: fn
      image: docker.io/library/hello-wasm:v1
---
apiVersion: v1
kind: Pod
metadata:
  name: linux-app
spec:
  runtimeClassName: runc          # 走传统容器
  containers:
    - name: app
      image: nginx:alpine

# 通过注解把宿主目录以只读方式映射进 wasm 模块
apiVersion: v1
kind: Pod
metadata:
  name: wasm-with-fs
  annotations:
    # runwasi 支持的挂载语义：仅开放 /data 只读
    wasm.containerd.io/mounts: "[]"
spec:
  runtimeClassName: wasmedge
  containers:
    - name: fn
      image: docker.io/library/reader-wasm:v1
      # 用只读 volume 替代宿主机全量能力开放
      volumeMounts:
        - { name: data, mountPath: /data, readOnly: true }
  volumes:
    - name: data
      hostPath: { path: /data, type: Directory }

# 用 openssl 生成 Dilithium 密钥（需支持 PQC 的 openssl 分支）
openssl genpkey -algorithm dilithium3 -out ca-dilithium.key
openssl req -x509 -new -key ca-dilithium.key \
  -out ca-dilithium.crt -days 3650

# 将新 CA 注入 Istio 的 cacerts，与旧 RSA CA 并存过渡
kubectl create secret generic cacerts -n istio-system \
  --from-file=ca-cert.pem=ca-dilithium.crt \
  --from-file=ca-key.pem=ca-dilithium.key \
  --dry-run=client -o yaml | kubectl apply -f -

# Envoy 启用混合密钥交换（X25519 + Kyber768），兼容又抗量子
tls_context:
  common_tls_context:
    tls_params:
      tls_minimum_protocol_version: TLSv1_3
    combined_certificate_validation_context:
      # 同时验证 RSA 与 Dilithium 双链
      verify_certificate_spki:
        - "<RSA_SPKI>"
        - "<DILITHIUM_SPKI>"
    alpn_protocols: ["h2", "http/1.1"]

# 调大 WireGuard/Istio 隧道接口的 MTU，容纳更大的量子签名握手包
ip link set mtu 1600 dev istio-tun
# 开启路径 MTU 发现，避免手动估值出错
sysctl -w net.ipv4.tcp_mtu_probing=1

// Scheduler Extender 的 Filter/Score 片段（简化）
func (e *Extender) Score(args *ExtenderArgs) *ExtenderScoreResult {
    node := args.Nodes.Items[0]
    carbon := queryCarbonIntensity(node.Region) // 查实时碳强度
    score := mapRange(carbon, low=100, high=0)  // 碳越低分越高
    return &ExtenderScoreResult{
        HostScores: []HostScore{{Name: node.Name, Score: int64(score)}},
    }
}

# 注册 Extender 到 kube-scheduler
apiVersion: v1
kind: ConfigMap
metadata:
  name: scheduler-policy
data:
  policy.cfg: |
    {
      "extenders": [{
        "urlPrefix": "http://carbon-scheduler.ext:8888",
        "filterVerb": "filter",
        "scoreVerb": "score",
        "weight": 5,
        "managedResources": [{"name": "carbon.example/region"}]
      }]
    }

# Kepler 导出 Pod 级能耗为 Prometheus 指标
kubectl apply -f https://github.com/sustainable-computing-io/kepler/releases/download/v0.7/kepler.yaml

# Carbon Aware SDK 拉取电网碳强度并预测
carbon-aware-cli \
  --location "east-china" \
  --duration 6h \
  --strategy "lowest-carbon" \
  --forecast

# 碳强度超阈值时，给批处理 Job 打暂停标签并冻结
INTENSITY=$(curl -s carbon.api/now)     # 取实时碳强度
if [ "$INTENSITY" -gt 500 ]; then        # 阈值 500 gCO2/kWh
  kubectl label job batch-etl paused=true --overwrite
  kubectl scale job batch-etl --replicas=0
  # 记录审计：谁、何时、因碳停
  echo "$(date) carbon=$INTENSITY paused batch-etl" >> /var/log/carbon-audit.log
fi

# 初始化 IPFS Cluster（带 raft 共识）
ipfs-cluster-service init
ipfs-cluster-service daemon &
# 添加节点到集群
ipfs-cluster-ctl add /path/to/model.bin   # 返回 CID

# IPFS Cluster 在 K8s 中以 StatefulSet 部署并设 Pin 策略
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: ipfs-cluster
  namespace: p2p
spec:
  serviceName: ipfs-cluster
  replicas: 3
  template:
    spec:
      containers:
        - name: cluster
          image: ipfs/ipfs-cluster:latest
          env:
            - name: CLUSTER_IPFSHTTP_PINPOLICY
              # 只允许 Pin 以 bafy 开头的模型 CID，防滥用
              value: '{"bafy": {"replication-min":2,"replication-max":5}}'

# Akash 部署单（SDL）：声明容器与竞价约束
version: "2.0"
services:
  web:
    image: nginx
    expose:
      - port: 80
      - port: 443
profiles:
  compute:
    web:
      resources:
        cpu: { units: 1.0 }
        memory: { size: 512Mi }
        storage: { size: 1Gi }
  placement:
    dcloud:
      pricing:
        web:
          denom: uakt
          amount: 1000        # 出价上限
deployment:
  web:
    dcloud:
      profile: web
      count: 1

# 网关超时时回退到本地 IPFS 节点，至少能返回内容哈希做校验
CID="bafybeigdyrzt5sfp7udm7hu76uh7y26nf3efuylqabf3oclgtqy55fbzdi"
if ! curl -sf "https://ipfs.io/ipfs/$CID" -o /tmp/out; then
  echo "网关超时，降级到本地节点"
  ipfs cat "$CID" > /tmp/out || echo "本地亦无，仅返回哈希供校验: $CID"
fi

# 强化学习调 HPA（伪代码骨架）
import gym, torch
class HPAMetaEnv(gym.Env):
    def step(self, action):  # action = [target_cpu, cooldown]
        apply_hpa(action)
        obs = get_metrics()                 # CPU、延迟、成本
        reward = -latency - 0.1*cost        # 稳且省得分高
        return obs, reward, done, {}
# 训练后把最优策略写回 HPA
best = agent.best_action()
kubectl patch hpa web --patch \
  "{\"spec\":{\"targetCPUUtilizationPercentage\":$best[0]}}"

# 自然语言 → PromQL → Grafana 面板（简化）
import openai, requests
def nl_to_dashboard(question: str, prom_url: str):
    # 1) 让 LLM 把自然语言转成 PromQL
    promql = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role":"user",
          "content": f"把下面问题转成 PromQL: {question}"}]
    )["choices"][0]["message"]["content"]
    # 2) 查 Prometheus 验证能跑
    r = requests.get(f"{prom_url}/api/v1/query", params={"query": promql})
    # 3) 生成 Grafana dashboard JSON 并导入
    panel = {"type":"timeseries","targets":[{"expr":promql}]}
    return import_to_grafana(panel)

# AIOps 决策闸门：AI 建议需经人工审批，且记录偏差用于复盘
apiVersion: v1
kind: ConfigMap
metadata:
  name: aiops-guard
data:
  policy: |
    max_scale_replicas: 20          # AI 不能无限扩
    require_human_above: 10         # 超 10 副本必须人工确认
    log_deviation: true             # 记录 AI 与 SRE 策略偏差
    fallback: "last-known-good"     # 冲突时回退到上次安全配置