تحليل داده هاي ترافيکي با هدف تشخيص ازدحام با بهره گيري از الگوريتم هاي يادگيري ماشين
الموضوعات :ناهید امانی 1 , محمدحسین عامری مهر 2 , سارا افاضاتی 3 , علی جاویدانی 4
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4 - دانشکده مهندسی برق و کامپیوتر، دانشکدگان فنی، دانشگاه تهران
الکلمات المفتاحية: تشخیص ازدحام, يادگيري ماشين, XGBoost, معیارهای ارزیابی الگوریتم های یادگیری ماشین,
ملخص المقالة :
شناسايي ازدحام يکي از ارکان پايه اي در تضمين کيفيت خدمت به عنوان مهمترين معيار سنجش کارايي در شبکه هاي مخابراتي نسل جديد است. ازدحام منجر به از دست رفتن بسته هاي اطلاعاتي در شبکه مي شود که به دليل بالاتر بودن نرخ ارسال از ظرفيت در برخي از لينک‌هاي شبکه رخ مي دهد. براي کنترل ازدحام در شبکه گام نخست تمايز ميان از دست رفتن اطلاعات در اثر ازدحام و يا در اثر ساير موارد از جمله خرابي لينک است زيرا در صورتي که منشا از دست رفتن بسته به اشتباه ازدحام تشخيص داده شود کاهش نرخ ارسال در فرستنده کمکي به کاهش ازدحام نکرده و تنها موجب کاهش گذردهي و کيفيت سرويس مي شود. از اين رو مسئله اصلي اين مقاله شناسايي ازدحام و تفکیک آن از خطاي ناشي از لينک هاي ارتباطي در يک نمونه داده ترافيکي است. در اين مقاله براي حل مسئله مذکور از الگوريتم هاي مختلف يادگيري ماشين نظارتشده از جمله درخت تصميم، جنگل تصادفي، ماشين بردار پشتيبان، لجيستيک رگرسيون، K-نزديکترين همسايه، XGBoost، شبکه عصبي و تقويت درخت تصميم بهره گرفته شده است. الگوريتم هاي مذکور بر اساس معيارهاي مختلف از جمله دقت، درستی، F1-measure، حساسیت و AUC مورد ارزيابي قرار گرفته و با يکديگر مقايسه شده اند. اين ارزيابي بر اساس روش K-fold Cross Validation انجام شده است. نتایج شبیه‌سازی نشان می‌دهد که الگوریتم XGBoost به لحاظ تمام معیارهای ارزیابی نسبت به دیگر الگوریتم‌ها عملکرد بهتری در زمینه تشخیص ازدحام دارد.
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