查看更多>>摘要：The researchers engaging to wide-area geographically distributed platform "Distcloud" have kept maintaining and operating the platform since the start of Distcloud, 2012. They have already lost their enthsiasm for expasion of Distcloud and development of Distcloud and DESTCloud that evaluate and validate fault tolerance of Distcloud have been slugging for a long time. Although an author have also sit around for years, he started to exert himself to the universe and his childfood dream again.
查看更多>>摘要：The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.
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