یک روش ترافیک‌آگاه برای دسته‌بندی بسته‌ها با هدف کاهش تعداد دفعات دسترسی به حافظه
محورهای موضوعی : مهندسی برق و کامپیوتر
سعید اسدروز
1
(دانشگاه بوعلی سینا)
محمد نصیری
2
(دانشگاه بوعلی سینا )
مهدی عباسی
3
(دانشگاه بوعلی سینا همدان)
حاتم عبدلی
4
(دانشگاه بوعلی سینا)
کلید واژه: ترافیک‌آگاهدرخت AVLدسته‌بندی بسته‌هامحلی‌بودن مراجعات,
چکیده مقاله :
دسته‌بندی بسته‌ها نقش بسزایی در بهبود عملکرد تجهیزات شبکه‌ای از جمله مسیریاب‌ها، دیواره‌های آتش و سیستم‌های تشخیص نفوذ ایفا می‌کند. الگوریتم‌های دسته‌بندی بسته عموماً مبتنی بر ساختار داده‌ای ایستا هستند که الگوی رفتاری ترافیک ورودی را در بهینه‌سازی ساختار جستجو در نظر نمی‌گیرند. در این پژوهش، ویژگی‌های آماری ترافیک ورودی در نظر گرفته شده و از ساختمان داده‌های کمکی ترافیک‌آگاه در کنار ساختارهای اصلی استفاده شده است. از آنجا که حجم غالب ترافیک اینترنت، مربوط به جریان‌های بلندمدت است، برای مدت‌زمانی نه چندان کوتاه، اکثر مطابقت‌های قوانین در زیردرخت‌های مشخصی از درخت جستجو قرار دارند. برای بهره‌گیری از این ویژگی، در این پژوهش از ساختار داده درخت AVL برای نگهداری قوانین دسته‌بند و از حدهای بالا و پایین مجموعه قوانین به عنوان گره‌های درخت جستجو استفاده شده است. ارزیابی‌ها نشان می‌دهد که با افزایش چولگی بسته‌های آزمون، تعداد دفعات دسترسی به حافظه الگوریتم دسته‌بندی ترافیک‌آگاه نسبت به الگوریتم دسته‌بندی پایه کاهش قابل توجهی دارد. بر اساس ارزیابی‌ها، دسته‌بندی بسته ترافیک‌آگاه با استفاده از قوانین پرتکرار می‌تواند میانگین کل تعداد دفعات دسترسی به حافظه و در نتیجه زمان جستجو را بیش از 40 درصد کاهش دهد.
Packet classification plays a critical role in improving the performance of many network devices including routers, firewalls and intrusion detection systems. Due to the increasing number of classification rules, high traffic volume and high bandwidth network links, designing an efficient packet classifier becomes more challenging. Packet classification algorithms that use static data structure do not consider the pattern of the incoming traffic in optimizing their search mechanism. Therefore, we use some statistical characteristics of the incoming traffic to propose a traffic aware data structure. Since most Internet traffic volume belong to long-live flows, the majority of the packets are matched to the rules in a few sub trees. To take the advantage of this feature, AVL tree data structure is served for storing classification rules where the upper and lower limits of the rule-set are used as nodes. Our evaluation have shown that with increasing the skewness of data packets, the average number of memory accesses are significantly decreased compared to the basic case. Finally, evaluation results show that the traffic-aware packet classification with high frequency rules can decrease more than 40% of the average number of memory accesses and consequently the lookup time.
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