{"id":12547,"date":"2024-04-10T11:35:48","date_gmt":"2024-04-10T03:35:48","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-sc\/new-trends-in-data-center-hosting-cloud-edge-collaboration-green-energy-ai-driven\/"},"modified":"2024-04-10T11:54:39","modified_gmt":"2024-04-10T03:54:39","slug":"new-trends-in-data-center-hosting-cloud-edge-collaboration-green-energy-ai-driven","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/new-trends-in-data-center-hosting-cloud-edge-collaboration-green-energy-ai-driven\/","title":{"rendered":"\u6570\u636e\u4e2d\u5fc3\u6258\u7ba1\u65b0\u8d8b\u52bf:\u4e91\u8fb9\u534f\u540c\u3001\u7eff\u8272\u8282\u80fd\u3001AI\u9a71\u52a8"},"content":{"rendered":"

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\u968f\u7740\u7269\u8054\u7f51\u30015G\u7b49\u65b0\u6280\u672f\u7684\u5d1b\u8d77,\u6570\u636e\u7684\u4ea7\u751f\u4e0e\u6d88\u8d39\u90fd\u5448\u73b0\u51fa\u8fb9\u7f18\u5316\u8d8b\u52bf\u3002\u4f20\u7edf\u7684\u96c6\u4e2d\u5f0f\u6570\u636e\u4e2d\u5fc3\u67b6\u6784\u5df2\u96be\u4ee5\u5e94\u5bf9\u6d77\u91cf\u3001\u5b9e\u65f6\u7684\u8fb9\u7f18\u6570\u636e\u3002\u4e91\u8fb9\u534f\u540c\u5e94\u8fd0\u800c\u751f,\u5176\u6838\u5fc3\u662f\u901a\u8fc7\u5206\u5c42\u89e3\u8026,\u5b9e\u73b0\u4e91\u3001\u8fb9\u3001\u7aef\u7684\u534f\u540c\u4e92\u8865\u3002\u5728\u8fd9\u4e00\u67b6\u6784\u4e0b,\u8fb9\u7f18\u8282\u70b9\u627f\u62c5\u8d77\u6570\u636e\u91c7\u96c6\u3001\u5b9e\u65f6\u5904\u7406\u7684\u4efb\u52a1,\u800c\u4e91\u7aef\u5219\u4e13\u6ce8\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5b58\u50a8\u3001\u6df1\u5ea6\u5b66\u4e60\u7b49\u91cd\u4efb\u3002\u901a\u8fc7\u52a8\u6001\u7f16\u6392\u4e0e\u667a\u80fd\u8c03\u5ea6,\u4e91\u8fb9\u8d44\u6e90\u53ef\u4ee5\u5b9e\u73b0\u6309\u9700\u5206\u914d\u3001\u5f39\u6027\u4f38\u7f29,\u5927\u5e45\u63d0\u5347\u8d44\u6e90\u5229\u7528\u6548\u7387\u3002<\/p>\n

# \u4f7f\u7528Kubernetes\u8fdb\u884c\u4e91\u8fb9\u8d44\u6e90\u7f16\u6392\r\napiVersion: v1\r\nkind: Pod\r\nmetadata:\r\n  name: edge-node\r\nspec:\r\n  containers:\r\n  - name: data-processor\r\n    image: data-processor:v1\r\n    resources:\r\n      limits:\r\n        cpu: 100m\r\n        memory: 128Mi\r\n  nodeSelector:\r\n    location: edge\r\n<\/code><\/pre>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
\u7eff\u8272\u8282\u80fd:\u53ef\u6301\u7eed\u53d1\u5c55\u7684\u5fc5\u7531\u4e4b\u8def<\/strong><\/h2>\n
\u968f\u7740\u5168\u7403\u73af\u4fdd\u610f\u8bc6\u7684\u63d0\u5347,\u7eff\u8272\u8282\u80fd\u5df2\u6210\u4e3a\u6570\u636e\u4e2d\u5fc3\u53d1\u5c55\u7684\u5fc5\u7531\u4e4b\u8def\u3002\u5148\u8fdb\u7684\u6db2\u51b7\u3001\u98ce\u51b7\u7b49\u6563\u70ed\u6280\u672f,\u53ef\u6709\u6548\u964d\u4f4e\u5236\u51b7\u80fd\u8017;\u592a\u9633\u80fd\u3001\u98ce\u80fd\u7b49\u53ef\u518d\u751f\u80fd\u6e90,\u5219\u4e3a\u6570\u636e\u4e2d\u5fc3\u63d0\u4f9b\u4e86\u6e05\u6d01\u7535\u529b\u3002\u66f4\u91cd\u8981\u7684\u662f,\u667a\u80fd\u80fd\u6548\u7ba1\u7406\u7cfb\u7edf\u53ef\u5b9e\u65f6\u76d1\u6d4bIT\u8bbe\u5907\u4e0e\u5236\u51b7\u7cfb\u7edf\u7684\u80fd\u8017\u6c34\u5e73,\u5e76\u901a\u8fc7\u667a\u80fd\u8c03\u5ea6\u5b9e\u73b0\u7535\u529b\u9700\u6c42\u54cd\u5e94,\u5728\u4fdd\u969c\u670d\u52a1\u8d28\u91cf\u7684\u540c\u65f6\u6700\u5927\u9650\u5ea6\u5730\u8282\u7ea6\u80fd\u6e90\u3002\u7eff\u8272\u8282\u80fd\u6b63\u5728\u6210\u4e3a\u6570\u636e\u4e2d\u5fc3\u7684\u65b0\u5e38\u6001\u3002<\/p>\n
\/\/ \u4f7f\u7528Python\u76d1\u63a7\u670d\u52a1\u5668CPU\u6e29\u5ea6\r\nimport psutil\r\n\r\ndef get_cpu_temp():\r\n    temp = psutil.sensors_temperatures()\r\n    if not temp:\r\n        return None\r\n    \r\n    for name, entries in temp.items():\r\n        if name.startswith('coretemp'):\r\n            return entries[0].current\r\n            \r\nwhile True:\r\n    cpu_temp = get_cpu_temp()\r\n    print(f\"CPU\u6e29\u5ea6: {cpu_temp}\u00b0C\")\r\n    \r\n    if cpu_temp > 80:\r\n        # \u89e6\u53d1\u544a\u8b66\u4e0e\u964d\u9891\u63aa\u65bd \r\n        send_alert(f\"CPU\u6e29\u5ea6\u8fc7\u9ad8: {cpu_temp}\u00b0C\")\r\n        underclock_cpu()\r\n        \r\n    time.sleep(60)  # \u6bcf\u5206\u949f\u68c0\u67e5\u4e00\u6b21\r\n<\/code><\/pre>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
AI\u8d4b\u80fd:\u667a\u80fd\u8fd0\u7ef4\u7684\u672a\u6765<\/strong><\/h2>\n
\u4eba\u5de5\u667a\u80fd\u6b63\u52a0\u901f\u6e17\u900f\u5230\u6570\u636e\u4e2d\u5fc3\u8fd0\u7ef4\u7684\u65b9\u65b9\u9762\u9762\u3002\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u53ef\u6301\u7eed\u5206\u6790\u6d77\u91cf\u8fd0\u7ef4\u6570\u636e,\u7cbe\u51c6\u9884\u6d4b\u8bbe\u5907\u6545\u969c,\u5b9e\u73b0\u9884\u6d4b\u6027\u7ef4\u62a4\u3002\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\u5219\u53ef\u667a\u80fd\u5316\u5904\u7406\u8fd0\u7ef4\u5de5\u5355,\u5927\u5e45\u63d0\u5347\u4eba\u5de5\u6548\u7387\u3002\u66f4\u4ee4\u4eba\u5174\u594b\u7684\u662f,\u5f3a\u5316\u5b66\u4e60\u6b63\u8d4b\u4e88\u6570\u636e\u4e2d\u5fc3”\u81ea\u6211\u4f18\u5316”\u7684\u80fd\u529b,\u7cfb\u7edf\u53ef\u81ea\u4e3b\u5b66\u4e60\u4f18\u5316\u7b56\u7565,\u52a8\u6001\u8c03\u6574\u8d44\u6e90\u914d\u7f6e\u4e0e\u4efb\u52a1\u8c03\u5ea6,\u5b9e\u73b0\u5168\u5c40\u6700\u4f18\u3002AI\u8ba9\u6570\u636e\u4e2d\u5fc3\u53d8\u5f97\u66f4\u52a0\u667a\u80fd\u3001\u97e7\u6027\u66f4\u5f3a\u3002<\/p>\n
# \u4f7f\u7528TensorFlow\u8fdb\u884c\u670d\u52a1\u5668\u8d1f\u8f7d\u9884\u6d4b\r\nimport tensorflow as tf\r\n\r\nmodel = tf.keras.Sequential([\r\n    tf.keras.layers.LSTM(128, input_shape=(30, 5)), \r\n    tf.keras.layers.Dense(64, activation='relu'),\r\n    tf.keras.layers.Dense(1)  \r\n])\r\n\r\nmodel.compile(optimizer='adam', loss='mse')\r\n\r\n# \u8bad\u7ec3\u6570\u636e: \u8fc7\u53bb30\u5929\u7684CPU\u3001\u5185\u5b58\u3001I\/O\u7b49\u6307\u6807\r\ntrain_data = [...]\r\n\r\nmodel.fit(train_data, epochs=100, batch_size=32)  \r\n\r\n# \u9884\u6d4b\u672a\u676524\u5c0f\u65f6\u7684\u8d1f\u8f7d\u8d8b\u52bf\r\nfuture_load = model.predict(recent_metrics)\r\n<\/code><\/pre>\n
\u7eb5\u89c2\u5168\u7403\u6570\u636e\u4e2d\u5fc3\u7684\u53d1\u5c55\u8d8b\u52bf,\u6211\u4eec\u770b\u5230\u4e86\u4e00\u4e2a\u66f4\u52a0\u667a\u80fd\u3001\u7eff\u8272\u3001\u654f\u6377\u7684\u672a\u6765\u3002\u4e91\u8fb9\u534f\u540c\u3001\u7eff\u8272\u8282\u80fd\u3001\u4eba\u5de5\u667a\u80fd\u7b49\u65b0\u6280\u672f,\u6b63\u5728\u6df1\u523b\u91cd\u5851\u670d\u52a1\u5668\u6258\u7ba1\u7684\u5f62\u6001\u4e0e\u5185\u6db5\u3002\u4f46\u6280\u672f\u6c38\u8fdc\u53ea\u662f\u624b\u6bb5,\u670d\u52a1\u624d\u662f\u6839\u672c\u3002\u65e0\u8bba\u6280\u672f\u5982\u4f55\u53d8\u8fc1,\u4ee5\u5ba2\u6237\u4e3a\u4e2d\u5fc3\u3001\u4ee5\u5e94\u7528\u4e3a\u5bfc\u5411,\u59cb\u7ec8\u662f\u6570\u636e\u4e2d\u5fc3\u7684\u4e0d\u4e8c\u6cd5\u95e8\u3002\u8ba9\u6211\u4eec\u643a\u624b\u5e76\u8fdb,\u5171\u540c\u5f00\u542f\u6570\u636e\u4e2d\u5fc3\u7684\u65b0\u7eaa\u5143![\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"
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