\nGrupp, L. M., Davis, J. D., & Swanson, S. (2012). The bleak future of NAND flash memory. In 10th {USENIX} Conference on File and Storage Technologies ({FAST} 12).<\/p>\n<\/li>\n<\/ol>\n<\/blockquote>\n
\u6839\u636e\u8fd9\u4e9b\u7406\u8bba\u4f9d\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff1a\u5728\u5904\u7406\u4e32\u53e3\u6570\u636e\u65f6\uff0c\u4f7f\u7528\u7f13\u51b2\u533a\uff08buf\uff09\u5148\u5b58\u50a8\u6570\u636e\uff0c\u7136\u540e\u518d\u8fdb\u884c\u6279\u91cf\u5199\u5165\u7684\u65b9\u5f0f\uff0c\u76f8\u6bd4\u4e8e\u6536\u5230\u6570\u636e\u540e\u7acb\u5373\u5199\u5165\uff0c\u66f4\u6709\u5229\u4e8e\u5ef6\u957feMMC\u82af\u7247\u7684\u5bff\u547d\u3002<\/p>\n
\u91cd\u65b0\u8bbe\u8ba1\u540e\u7684\u4f2a\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import os\nimport zipfile\nimport asyncio\nimport serial_asyncio\nimport configparser\nfrom datetime import datetime, timedelta\nimport re\nimport logging\nfrom logging.handlers import TimedRotatingFileHandler\nimport traceback\n\nShutDownFlag = "AGV Key Shutdown"\n\n# pre-compile regex\nre_printable_ascii = re.compile(r'[^\\x20-\\x7E]+')\nre_log_file_suffix = re.compile(r"^\\d{4}-\\d{2}-\\d{2}.log$")\n\n# read config file\nconfig = configparser.ConfigParser()\nconfig.read("\/etc\/xx.ini", encoding="utf-8")\n\n# read serial config\nserialPort = config.get("SERIAL_INFO", "serialPort")\nserialBaudrate = config.getint("SERIAL_INFO", "serialBaudrate")\nserialDatabits = config.getint("SERIAL_INFO", "serialDatabits")\nserialStopbits = config.getint("SERIAL_INFO", "serialStopbits")\nserialTimeout = config.getint("SERIAL_INFO", "serialTimeout")\n\n# read log config\nlogPath = config.get("LOG_INFO", "logPath")\nlogRetention = config.get("LOG_INFO", "logRetention")\nlogRotaSize = config.get("LOG_INFO", "logRotaSize")\nlogRotaTime = config.get("LOG_INFO", "logRotaTime")\nlogCompression = config.get("LOG_INFO", "logCompression")\nlogEncoding = config.get("LOG_INFO", "logEncoding")\n\n# get log retention days\nlog_retention_days = int(''.join(filter(str.isdigit, logRetention)))\n\nBUFFER_SIZE = xx # Set buffer size according to your requirement\nlog_buffer = []\n\n# Configure logging\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n# Create log file handler with rotation\nfile_handler = TimedRotatingFileHandler(\n logPath, when='midnight', backupCount=log_retention_days, delay=True)\nfile_handler.setFormatter(logging.Formatter('%(message)s'))\n# Set the log file suffix to include the date\nfile_handler.suffix = "%Y-%m-%d.log"\n# Match log files with the correct suffix\nfile_handler.extMatch = re_log_file_suffix\n\n# Set the handler to append mode\nfile_handler.mode = 'a'\n\nlogger.addHandler(file_handler)\n\nRSDEXCEPTION = None\nRUNEXCEPTION = None\nZIP_EXCEPTION = None\n\nasync def handle_serial_data(reader, writer):\n global RSDEXCEPTION, log_buffer, BUFFER_SIZE\n\n while True:\n try:\n ...\n if byte_data and len(byte_data) > 3:\n ..\n # Keep only printable ASCII characters\n ...\n\n # Get the current time when the data is received\n ..\n\n # Add log with timestamp to buffer\n ...\n\n if ShutDownFlag in str_data: # if agv shutdown, then shutdown this program\n BUFFER_SIZE = 1\n\n # If buffer reaches its limit, write logs to file and reset buffer\n if len(log_buffer) >= BUFFER_SIZE:\n await write_log(log_buffer)\n log_buffer = [] # Reset the buffer after writing logs\n else:\n pass\n\n except Exception as e:\n if e != RSDEXCEPTION:\n logger.error("Receive SerialData error: {0}", type(e).__name__)\n RSDEXCEPTION = e\n elif e == RSDEXCEPTION:\n print("dump!", e)\n\n await write_log(log_buffer) # Write logs to file before exiting\n\n else:\n RSDEXCEPTION = None\n\n # Ensure that logs in the buffer are written to the file before the function exits\n finally:\n pass\n\ndef write_log_sync(log_buffer):\n logs_to_write = '\\n'.join(log_buffer)\n logger.info(logs_to_write)\n\n # Flush the logger to ensure logs are written to the file\n logger.handlers[0].flush()\n\nasync def write_log(log_buffer):\n loop = asyncio.get_event_loop()\n await loop.run_in_executor(None, write_log_sync, log_buffer)\n\nasync def zip_log_file():\n global ZIP_EXCEPTION\n while True:\n try:\n ...\n if os.path.exists(logPath) and os.path.getsize(logPath) > 0:\n ..\n with zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED) as zf:\n zf.write(logPath, arcname=os.path.basename(logPath))\n\n os.remove(logPath)\n except Exception as e:\n if e != ZIP_EXCEPTION:\n logger.error("zip_log_file error: {0}", type(e).__name__)\n print("zip_log_file error: {0}", type(e).__name__)\n ZIP_EXCEPTION = e\n else:\n ZIP_EXCEPTION = None\n\nasync def main():\n # Check if log file exists and is not empty, zip and remove it before starting the program\n if os.path.exists(logPath) and os.path.getsize(logPath) > 0:\n ..\n\n with zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED) as zf:\n zf.write(logPath, arcname=os.path.basename(logPath))\n\n os.remove(logPath)\n\n global RUNEXCEPTION\n\n try:\n loop = asyncio.get_event_loop()\n reader, writer = await serial_asyncio.open_serial_connection(\n loop=loop, url=serialPort, baudrate=serialBaudrate\n )\n\n except Exception:\n exc = traceback.format_exc()\n if exc != RUNEXCEPTION:\n logger.error("main error: {0}", exc)\n RUNEXCEPTION = exc\n return\n else:\n RUNEXCEPTION = None\n\n try:\n asyncio.create_task(zip_log_file())\n await handle_serial_data(reader, writer)\n finally:\n writer.close()\n await writer.wait_closed()\n\nif __name__ == '__main__':\n try:\n asyncio.run(main())\n except Exception as e:\n logger.exception("main error!", type(e).__name__)\n<\/code><\/pre>\n\u5173\u4e8e\u5f02\u6b65\u903b\u8f91\u7684\u4f7f\u7528\uff0c\u9700\u8981\u6839\u636e\uff1a<\/p>\n
\n- I\/O \u5bc6\u96c6\u578b\u64cd\u4f5c<\/strong>\uff1a\u5f02\u6b65\u7f16\u7a0b\u7279\u522b\u9002\u7528\u4e8e I\/O \u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u5982\u7f51\u7edc\u8bf7\u6c42\u3001\u6587\u4ef6\u8bfb\u5199\u7b49\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5f02\u6b65\u7f16\u7a0b\u53ef\u4ee5\u907f\u514d\u963b\u585e\u4e3b\u7ebf\u7a0b\uff0c\u4ece\u800c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6027\u80fd\u548c\u54cd\u5e94\u901f\u5ea6\u3002<\/li>\n
- \u5e76\u53d1<\/strong>\uff1a\u5982\u679c\u7a0b\u5e8f\u9700\u8981\u540c\u65f6\u5904\u7406\u591a\u4e2a\u4efb\u52a1\uff0c\u5f02\u6b65\u7f16\u7a0b\u53ef\u4ee5\u5b9e\u73b0\u4efb\u52a1\u4e4b\u95f4\u7684\u5e76\u53d1\u6267\u884c\u3002\u8fd9\u53ef\u4ee5\u5728\u4e0d\u589e\u52a0\u989d\u5916\u7ebf\u7a0b\u5f00\u9500\u7684\u60c5\u51b5\u4e0b\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u541e\u5410\u91cf\u3002<\/li>\n
- \u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u5f02\u6b65\u7f16\u7a0b\u5728\u5904\u7406\u5927\u91cf\u8bf7\u6c42\u65f6\u6709\u66f4\u597d\u7684\u53ef\u6269\u5c55\u6027\u3002\u4e0e\u591a\u7ebf\u7a0b\u76f8\u6bd4\uff0c\u5f02\u6b65\u7f16\u7a0b\u901a\u5e38\u4f7f\u7528\u66f4\u5c11\u7684\u7cfb\u7edf\u8d44\u6e90\uff08\u5982\u5185\u5b58\u3001\u7ebf\u7a0b\u7b49\uff09\uff0c\u56e0\u6b64\u5728\u8d44\u6e90\u6709\u9650\u7684\u73af\u5883\u4e2d\u4f1a\u66f4\u52a0\u9ad8\u6548\u3002<\/li>\n
- \u54cd\u5e94\u6027<\/strong>\uff1a\u5bf9\u4e8e\u9700\u8981\u5feb\u901f\u54cd\u5e94\u7528\u6237\u64cd\u4f5c\u6216\u5176\u4ed6\u4e8b\u4ef6\u7684\u7a0b\u5e8f\uff0c\u5f02\u6b65\u7f16\u7a0b\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u6301\u7a0b\u5e8f\u7684\u54cd\u5e94\u6027\u3002\u901a\u8fc7\u907f\u514d\u963b\u585e\u4e3b\u7ebf\u7a0b\uff0c\u5f02\u6b65\u7f16\u7a0b\u786e\u4fdd\u7a0b\u5e8f\u80fd\u591f\u5728\u5904\u7406\u8017\u65f6\u64cd\u4f5c\u65f6\u4ecd\u7136\u5bf9\u7528\u6237\u64cd\u4f5c\u505a\u51fa\u53ca\u65f6\u54cd\u5e94\u3002<\/li>\n
- \u4efb\u52a1\u4f9d\u8d56\u5173\u7cfb<\/strong>\uff1a\u5f02\u6b65\u7f16\u7a0b\u5728\u5904\u7406\u5177\u6709\u590d\u6742\u4f9d\u8d56\u5173\u7cfb\u7684\u4efb\u52a1\u65f6\u5177\u6709\u4f18\u52bf\u3002\u901a\u8fc7\u4f7f\u7528
asyncio<\/code> \u7b49\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ec4\u7ec7\u548c\u7f16\u6392\u4efb\u52a1\uff0c\u786e\u4fdd\u5b83\u4eec\u6309\u7167\u6b63\u786e\u7684\u987a\u5e8f\u548c\u4f18\u5148\u7ea7\u6267\u884c\u3002<\/li>\n<\/ol>\n\u8d44\u6e90\u5360\u7528\u76d1\u63a7<\/h3>\n
\u4e3a\u4e86\u6d4b\u8bd5\u65b0\u65b9\u6848\u7684\u6548\u679c\uff0c\u4f7f\u7528psutil<\/code>\u83b7\u53d6\u7cfb\u7edf\u8d44\u6e90\u6570\u636e\u6bd4\u8f83\u4e24\u8005\u5dee\u522b\uff0c<\/p>\n\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import time\nfrom datetime import datetime\nimport psutil\nimport csv\n\n# Interval in seconds\nINTERVAL = 5\n\n# Output file\nOUTPUT_FILE = "xx.csv"\nSUMMARY_FILE = "xx.csv"\n\n# Number of top processes to display\nNUM_PROCESSES = 7\n\n# Write header to the output file\nwith open(OUTPUT_FILE, "w", newline="") as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow(["Timestamp", "PID", "Process Name", "Read Bytes (B)", "Write Bytes (B)", "CPU Time (s)", "RAM Usage (B)", "Open Files", "Read Count", "Write Count"])\n\nprev_io_data = {}\ntotal_io_data = {}\n\ntry:\n while True:\n # Get a list of all running processes\n processes = [proc for proc in psutil.process_iter()]\n\n # Get I\/O and CPU data for each process and store it in a list\n process_io_data = []\n for process in processes:\n try:\n io_data = process.io_counters()\n read_bytes = io_data.read_bytes\n write_bytes = io_data.write_bytes\n read_count = io_data.read_count\n write_count = io_data.write_count\n process_path = process.exe()\n process_cmdline = " ".join(process.cmdline())\n\n cpu_time = process.cpu_times().user + process.cpu_times().system\n\n # Calculate the difference in read and write bytes and counts from last interval\n if process.pid in prev_io_data:\n prev_read_bytes, prev_write_bytes, prev_cpu_time, prev_read_count, prev_write_count = prev_io_data[process.pid]\n read_bytes_diff = read_bytes - prev_read_bytes\n write_bytes_diff = write_bytes - prev_write_bytes\n read_count_diff = read_count - prev_read_count\n write_count_diff = write_count - prev_write_count\n cpu_time_diff = cpu_time - prev_cpu_time\n else:\n read_bytes_diff = read_bytes\n write_bytes_diff = write_bytes\n read_count_diff = read_count\n write_count_diff = write_count\n cpu_time_diff = cpu_time\n\n prev_io_data[process.pid] = (read_bytes, write_bytes, cpu_time, read_count, write_count)\n\n # Get RAM usage (resident set size) for the process\n ram_usage = process.memory_info().rss\n\n # Get the list of open files for the process\n open_files = process.open_files()\n open_files_list = [open_file.path for open_file in open_files]\n\n process_io_data.append((process.pid, process_cmdline, read_bytes_diff, write_bytes_diff, cpu_time_diff, ram_usage, open_files_list, read_count_diff, write_count_diff))\n\n # Update the total I\/O data\n if process.pid in total_io_data:\n process_name, total_read_bytes, total_write_bytes, total_cpu_time = total_io_data[process.pid]\n total_read_bytes += read_bytes_diff\n total_write_bytes += write_bytes_diff\n total_cpu_time += cpu_time_diff\n else:\n process_name = process_cmdline\n total_read_bytes = read_bytes_diff\n total_write_bytes = write_bytes_diff\n total_cpu_time = cpu_time_diff\n\n total_io_data[process.pid] = (process_name, total_read_bytes, total_write_bytes, total_cpu_time)\n\n except (psutil.AccessDenied, psutil.NoSuchProcess):\n pass\n\n # Sort the list by total I\/O (read + write) in descending order\n process_io_data.sort(key=lambda x: x[2] + x[3], reverse=True)\n\n # Get the top I\/O processes\n top_io_processes = process_io_data[:NUM_PROCESSES]\n\n # Get the current timestamp\n timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")\n\n # Append the top I\/O processes data to OUTPUT_FILE\n with open(OUTPUT_FILE, "a", newline="") as f:\n csv_writer = csv.writer(f)\n for proc in top_io_processes:\n csv_writer.writerow([timestamp, proc[0], proc[1], proc[2], proc[3], proc[4], proc[5], ", ".join(proc[6]), proc[7], proc[8]])\n\n # Wait for the specified interval\n time.sleep(INTERVAL)\nexcept KeyboardInterrupt:\n # Sort the total I\/O data by total I\/O (read + write) in descending order\n sorted_total_io_data = sorted(total_io_data.items(), key=lambda x: x[1][1]+ x[1][2], reverse=True)\n\n # Write the summary to SUMMARY_FILE\n with open(SUMMARY_FILE, "w", newline="") as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow(["PID", "Process Name", "Total Read Bytes (B)", "Total Write Bytes (B)", "Total CPU Time (s)"])\n for proc in sorted_total_io_data:\n csv_writer.writerow([proc[0], proc[1][0], proc[1][1], proc[1][2], proc[1][3]])<\/code><\/pre>\n\u6839\u636ecsv\u6587\u4ef6\u7ed8\u5236\u66f2\u7ebf\u56fe\u76f4\u89c2\u6bd4\u8f83\uff1a<\/p>\n
import pandas as pd\nimport matplotlib.pyplot as plt\nimport sys\nfrom datetime import datetime\nfrom dateutil import parser\n\ndef parse_log(file_path):\n data = pd.read_csv(file_path)\n data['Timestamp'] = pd.to_datetime(data['Timestamp'])\n return data\n\ndef find_shortest_period(df1, df2):\n start1, end1 = df1['Timestamp'].min(), df1['Timestamp'].max()\n start2, end2 = df2['Timestamp'].min(), df2['Timestamp'].max()\n\n if start1 < start2:\n common_start = start2\n else:\n common_start = start1\n\n if end1 < end2:\n common_end = end1\n else:\n common_end = end2\n\n return common_start, common_end\n\ndef compare_logs(log1, log2, pid1, pid2):\n df1 = parse_log(log1)\n df2 = parse_log(log2)\n\n process_name1 = df1[df1['PID'] == pid1]['Process Name'].iloc[0]\n process_name2 = df2[df2['PID'] == pid2]['Process Name'].iloc[0]\n\n df1 = df1[df1['PID'] == pid1].reset_index(drop=True)\n df2 = df2[df2['PID'] == pid2].reset_index(drop=True)\n\n # Determine the longest period\n maxlen = min(len(df1), len(df2))\n\n # Pad the shorter dataframe with NaNs\n if len(df1) < maxlen:\n df1 = df1.reindex(range(maxlen))\n elif len(df2) < maxlen:\n df2 = df2.reindex(range(maxlen))\n\n # Replace NaNs with zeros\n df1.fillna(0, inplace=True)\n df2.fillna(0, inplace=True)\n\n # Remove rows where both data points are zero\n mask = (df1['Write Bytes (B)'] != 0) | (df2['Write Bytes (B)'] != 0) | (df1['CPU Time (s)'] != 0) | (df2['CPU Time (s)'] != 0)\n df1 = df1[mask]\n df2 = df2[mask]\n\n # Plotting\n fig, (ax1, ax2, ax3,ax4,ax5) = plt.subplots(5, 1, figsize=(10, 15))\n\n # Plot Write Bytes (B)\n ax1.plot(df1.index, df1['Write Bytes (B)'], label=f"{process_name1} - PID {pid1}", color='b')\n ax1.plot(df2.index, df2['Write Bytes (B)'], label=f"{process_name2} - PID {pid2}", color='r')\n ax1.set_xlabel('Count')\n ax1.set_ylabel('Write Bytes (B)')\n ax1.legend()\n\n # Plot Write Count\n ax2.plot(df1.index, df1['Write Count'], label=f"{process_name1} - PID {pid1}", color='b')\n ax2.plot(df2.index, df2['Write Count'], label=f"{process_name2} - PID {pid2}", color='r')\n ax2.set_xlabel('Count')\n ax2.set_ylabel('Write Count')\n ax2.legend()\n\n # Plot RAM Usage (B)\n ax3.plot(df1.index, df1['RAM Usage (B)'], label=f"{process_name1} - PID {pid1}", color='b')\n ax3.plot(df2.index, df2['RAM Usage (B)'], label=f"{process_name2} - PID {pid2}", color='r')\n ax3.set_xlabel('Count')\n ax3.set_ylabel('RAM Usage (B)')\n ax3.legend()\n\n # Plot CPU Usage\n ax4.plot(df1.index, df1['CPU Time (s)'], label=f"{process_name1} - PID {pid1}", color='b')\n ax4.plot(df2.index, df2['CPU Time (s)'], label=f"{process_name2} - PID {pid2}", color='r')\n ax4.set_xlabel('Count')\n ax4.set_ylabel('CPU Time (s)')\n ax4.legend()\n\n # Plot RAM Usage (B)\n ax5.plot(df1.index, df1['RAM Usage (B)'], label=f"{process_name1} - PID {pid1}", color='b')\n ax5.plot(df2.index, df2['RAM Usage (B)'], label=f"{process_name2} - PID {pid2}", color='r')\n ax5.set_xlabel('Count')\n ax5.set_ylabel('RAM Usage (B)')\n ax5.legend()\n\n plt.show()\n\n # Calculate IO and CPU pressure\n df1['IO Pressure'] = df1['Write Bytes (B)']\n df2['IO Pressure'] = df2['Write Bytes (B)']\n\n io_pressure_diff = df1['IO Pressure'].sum() - df2['IO Pressure'].sum()\n cpu_usage_diff = df1['CPU Time (s)'].sum() - df2['CPU Time (s)'].sum()\n\n # Generate report\n report = f"""Report for comparing IO and CPU usage between {log1} (PID {pid1}) and {log2} (PID {pid2}):\n\nCharts:\n1. Write Bytes (B): Shows the Write Bytes (B) for each process during the same period.\n Y-axis: Write Bytes (B)\n\n2. CPU Usage: Displays the CPU usage for each process during the same period.\n Y-axis: CPU Usage (in CPU Time (s))\n\nPlease refer to the displayed charts for a visual comparison.\n\nResults:\n- IO Pressure Difference: {io_pressure_diff}\n (Positive value means {log1} has more IO pressure, Negative value means {log2} has more IO pressure)\n\n- CPU Usage Difference: {cpu_usage_diff}\n (Positive value means {log1} has higher CPU usage, Negative value means {log2} has higher CPU usage)\n"""\n print(report)\n\nif __name__ == '__main__':\n log1 = "xx.csv"\n log2 = "xx.csv"\n pid1 = xx\n pid2 = xx\n compare_logs(log1, log2, pid1, pid2)<\/code><\/pre>\n\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n
![\"Figure_1.png\"](\"https:\/\/kanghaov-img-1256185664.cos.ap-shanghai.myqcloud.com\/2023\/07\/07\/c375763c37100.png\")
<\/p>\n
\u53ef\u4ee5\u5f88\u76f4\u89c2\u7684\u770b\u5230\u5728\u7f13\u5b58\u8bbe\u7f6e\u4e3a5\u7684\u60c5\u51b5\u4e0b\uff0c\u660e\u663e\u964d\u4f4eIO\u8bfb\u5199\u6b21\u6570\uff0c\u540c\u65f6\u5185\u5b58\u53caCPU\u5360\u7528\u4e5f\u4f18\u4e8e\u4f7f\u7528loguru<\/code>\uff0c\u540c\u65f6\u5728\u63a5\u6536\u673a\u5668\u4eba\u5173\u673a\u4fe1\u53f7\u80fd\u591f\u53ca\u65f6\u4fdd\u5b58\u7f13\u5b58\u4e2d\u7684\u65e5\u5fd7\uff0c\u6ee1\u8db3\u9884\u671f\u3002<\/p>\n~<\/p>\n
(\u8d81\u7740\u4e0b\u73ed\u524d\u4e00\u5c0f\u65f6\u603b\u7ed3\u7684~ \u732a\u8089\u672b\u4e00\u5757)<\/p>\n","protected":false},"excerpt":{"rendered":"
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Wear Leveling\u6280\u672f \u4e3a\u4e86\u5ef6\u957feMMC\u82af\u7247\u7684\u8bfb\u5199\u5bff\u547d\uff0c\u82af\u7247\u63a7\u5236\u5668\u901a\u5e38\u4f1a\u91c7\u7528\u4e00\u79cd\u79f0\u4e3aWear Leveling\u7684\u6280\u672f\u3002Wear Leveling\u53ef\u4ee5\u5c06\u64e6\u9664\u548c\u5199\u5165\u64cd\u4f5c\u5728\u6574\u4e2a\u82af\u7247\u7684\u5b58\u50a8\u5355\u5143\u4e0a\u5747\u5300\u5206\u5e03\uff0c\u4ece\u800c\u907f\u514d\u67d0\u4e9b\u5b58\u50a8\u5355\u5143\u8fc7\u65e9\u5931\u6548\u3002\u5c06\u591a\u6b21\u7684\u5c0fIO\u5408\u5e76\u6210\u4e00\u4e2a\u5927IO\u53ef\u4ee5\u964d\u4f4eWear Leveling\u7b97\u6cd5\u7684\u590d\u6742\u6027\uff0c\u63d0\u9ad8\u5176\u6548\u679c\u3002 \u7efc\u4e0a\u6240\u8ff0\uff0c\u5c06\u591a\u6b21\u7684\u5c0fIO\u5408\u5e76\u6210\u4e00\u4e2a\u5927IO\u5bf9eMMC\u82af\u7247\u7684\u8bfb\u5199\u5bff\u547d\u6709\u79ef\u6781\u5f71\u54cd\u3002\u8fd9\u79cd\u64cd\u4f5c\u65b9\u5f0f\u53ef\u4ee5\u964d\u4f4e\u64e6\u9664\u548c\u5199\u5165\u6b21\u6570\uff0c\u63d0\u9ad8\u82af\u7247\u7684\u6027\u80fd\uff0c\u5e76\u6709\u5229\u4e8eWear Leveling\u6280\u672f\u7684\u5b9e\u73b0\u3002\u5f53\u7136\uff0c\u8fd9\u79cd\u4f18\u5316\u9700\u8981\u5728\u4fdd\u8bc1\u6570\u636e\u5b8c\u6574\u6027\u548c\u5b9e\u65f6\u6027\u7684\u524d\u63d0\u4e0b \u53c2\u8003\u6587\u732e\uff1a Micheloni, R., Crippa, L., & Marelli, A. (2010). Inside NAND Flash Memories. Springer Science & Business Media. Grupp, L. M., Davis, J. D., & Swanson, S. (2012). The bleak […]<\/p>\n","protected":false},"author":1,"featured_media":1025,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[86,2,7],"tags":[79,102,18,10,116,98,130],"class_list":["post-981","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev","category-py","category-robot","tag-agv","tag-amr","tag-matplotlib","tag-python","tag-116","tag-98","tag-130"],"yoast_head":"\n
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(2010). Inside NAND Flash Memories. Springer Science & Business Media. Grupp, L. M., Davis, J. D., & Swanson, S. (2012). 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Wear Leveling\u6280\u672f \u4e3a\u4e86\u5ef6\u957feMMC\u82af\u7247\u7684\u8bfb\u5199\u5bff\u547d\uff0c\u82af\u7247\u63a7\u5236\u5668\u901a\u5e38\u4f1a\u91c7\u7528\u4e00\u79cd\u79f0\u4e3aWear Leveling\u7684\u6280\u672f\u3002Wear Leveling\u53ef\u4ee5\u5c06\u64e6\u9664\u548c\u5199\u5165\u64cd\u4f5c\u5728\u6574\u4e2a\u82af\u7247\u7684\u5b58\u50a8\u5355\u5143\u4e0a\u5747\u5300\u5206\u5e03\uff0c\u4ece\u800c\u907f\u514d\u67d0\u4e9b\u5b58\u50a8\u5355\u5143\u8fc7\u65e9\u5931\u6548\u3002\u5c06\u591a\u6b21\u7684\u5c0fIO\u5408\u5e76\u6210\u4e00\u4e2a\u5927IO\u53ef\u4ee5\u964d\u4f4eWear Leveling\u7b97\u6cd5\u7684\u590d\u6742\u6027\uff0c\u63d0\u9ad8\u5176\u6548\u679c\u3002 \u7efc\u4e0a\u6240\u8ff0\uff0c\u5c06\u591a\u6b21\u7684\u5c0fIO\u5408\u5e76\u6210\u4e00\u4e2a\u5927IO\u5bf9eMMC\u82af\u7247\u7684\u8bfb\u5199\u5bff\u547d\u6709\u79ef\u6781\u5f71\u54cd\u3002\u8fd9\u79cd\u64cd\u4f5c\u65b9\u5f0f\u53ef\u4ee5\u964d\u4f4e\u64e6\u9664\u548c\u5199\u5165\u6b21\u6570\uff0c\u63d0\u9ad8\u82af\u7247\u7684\u6027\u80fd\uff0c\u5e76\u6709\u5229\u4e8eWear Leveling\u6280\u672f\u7684\u5b9e\u73b0\u3002\u5f53\u7136\uff0c\u8fd9\u79cd\u4f18\u5316\u9700\u8981\u5728\u4fdd\u8bc1\u6570\u636e\u5b8c\u6574\u6027\u548c\u5b9e\u65f6\u6027\u7684\u524d\u63d0\u4e0b \u53c2\u8003\u6587\u732e\uff1a Micheloni, R., Crippa, L., & Marelli, A. (2010). Inside NAND Flash Memories. Springer Science & Business Media. Grupp, L. M., Davis, J. D., & Swanson, S. (2012). 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