A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding strategy (NNTF) is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively

An efficient pending interest table control management in named data network

Named Data Networking (NDN) is an emerging Internet architecture that employs a new network communication model based on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves a significant role of recording Interest packet information which is ready to be sent but in waiting for matching Data packet. In managing PIT, the issue of flow PIT sizing has been very challenging due to massive use of long Interest lifetime particularly when there is no flexible replacement policy, hence affecting PIT performance. The aim of this study is to propose an efficient PIT Control Management (PITCM) approach to be used in handling incoming Interest packets in order to mitigate PIT overflow thus enhancing PIT utilization and performance. PITCM consists of Adaptive Virtual PIT (AVPIT) mechanism, Smart Threshold Interest Lifetime (STIL) mechanism and Highest Lifetime Least Request (HLLR) policy. The AVPIT is responsible for obtaining early PIT overflow prediction and reaction. STIL is meant for adjusting lifetime value for incoming Interest packet while HLLR is utilized for managing PIT entries in efficient manner. A specific research methodology is followed to ensure that the work is rigorous in achieving the aim of the study. The network simulation tool is used to design and evaluate PITCM. The results of study show that PITCM outperforms the performance of standard NDN PIT with 45% higher Interest satisfaction rate, 78% less Interest retransmission rate and 65% less Interest drop rate. In addition, Interest satisfaction delay and PIT length is reduced significantly to 33% and 46%, respectively. The contribution of this study is important for Interest packet management in NDN routing and forwarding systems. The AVPIT and STIL mechanisms as well as the HLLR policy can be used in monitoring, controlling and managing the PIT contents for Internet architecture of the future

A hybrid rate control mechanism for forwarding and congestion control in named data network

Named Data Networking (NDN) is an emerging Internet architecture that employs a pull-based, in-path caching, hop-by-hop, and multi-path transport architecture. Therefore, transport algorithms which use conventional paradigms would not work correctly in the NDN environment, since the content source location frequently changes. These changes raise forwarding and congestion control problems, and they directly affect the link utilization, fairness, and stability of the network. This study proposes a Hybrid Rate Control Mechanism (HRCM) to control the forwarding rate and link congestion to enhance network scalability, stability, and fairness performance. HRCM consists of three schemes namely Shaping Deficit Weight Round Robin (SDWRR), Queue-delay Parallel Multipath (QPM), and Explicit Control Agile-based conservative window adaptation (EC-Agile). The SDWRR scheme is scheduling different flows in router interfaces by fairly detecting and notifying the link congestion. The QPM scheme has been designed to forward Interest packets to all available paths that utilize idle bandwidths. The EC-Agile scheme controls forwarding rates by examining each packet received. The proposed HRCM was evaluated by comparing it with two different mechanisms, namely Practical Congestion Control (PCON) and Hop-by-hop Interest Shaping (HIS) through ndnSIM simulation. The findings show that HRCM enhances the forwarding rate and fairness. HRCM outperforms HIS and PCON in terms of throughput by 75%, delay 20%, queue length 55%, link utilization 41%, fairness 20%, and download time 20%. The proposed HRCM contributes to providing an enhanced forwarding rate and fairness in NDN with different types of traffic flow. Thus, the SDWRR, QPM, and EC-Agile schemes can be used in monitoring, controlling, and managing congestion and forwarding for the Internet of the future