Design, Analysis, and Optimization of Traffic Engineering for Software Defined Networks

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting with TE and evaluating TE systems efficiently as this tool employs parallel processing to achieve high efficiency. The purpose of the simulator is two aspects: (1) We use it to understand traffic engineering, (2) we use it to formulate a new traffic engineering algorithm that is near-optimal and applicable in practice. We study the design of some important aspects of any TE system. In particular, the consequences of achieving optimal TE by solving the multi-commodity flow problem (MCF) and the consequences of choosing single-path routing over multi-path routing. With the help of the TE simulator, we compare many TE systems constructed by combining different paths selection techniques with two objective functions for rate adaptations: load balancing (LB) and average delay (AD). The results confirm that paths selected based on the theoretical approach known as Oblivious Routing combined with AD objective function can significantly increase the performance in terms of throughput, congestion, and delay.\par However, the new proposed system comes with a cost. The AD function has a higher complexity than the LB function. We show that this problem can be tackled by training deep learning models. We trained two models with two different neural network architectures: Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM), to get a responsive traffic engineering system. The input training data is based on synthetic data obtained from the simulator. The output of the two models is the split ratios that the SDN controller uses to instruct the switching devices about how to forward traffic in the network. The result confirms that both models are effective and can be used to forward traffic in an optimal or near-optimal way. The LSTM model has shown a slightly better result than MLP due to its ability to predict a longer output sequence

A Reinforcement Learning Framework with Region-Awareness and Shared Path Experience for Efficient Routing in Networks-on-Chip

Network-on-chip (NoC) architectures provide a scalable, high-performance, and reliable interconnect for emerging manycore systems. The routing policies used in NoCs have a significant impact on overall performance. Prior efforts have proposed reinforcement learning (RL)-based adaptive routing policies to avoid congestion and minimize latency in NoCs. The output quality of RL policies depends on selecting a representative cost function and an effective update mechanism. Unfortunately, existing RL policies for NoC routing fail to represent path contention and regional congestion in the cost function. Moreover, the experience of packet flows sharing the same route is not fully incorporated into the RL update mechanism. In this paper, we present a novel regional congestion-aware RL-based NoC routing policy called Q-RASP that is capable of sharing experience from packets using the same routes. Q-RASP improves average packet latency by up to 18.3% and reduces NoC energy consumption by up to 6.7% with minimal area overheads compared to state-of-the-art RL-based NoC routing implementations

Performance Evaluation of XY and XTRANC Routing Algorithm for Network on Chip and Implementation using DART Simulator

In today’s world Network on Chip(NoC) is one of the most efficient on chip communication platform for System on Chip where a large amount of computational and storage blocks are integrated on a single chip. NoCs are scalable and have tackled the short commings of SoCs . In the first part of this project the basics of NoCs is explained which includes why we should use NoC , how to implement NoC ,various blocks of NoCs .The next part of the project deals with the implementation of XY routing algorithm in mesh (3*3) and mesh (4*4) network topologies. The throughput and latency curves for both the topologies were found and a through comparison was done by varying the no of virtual cannels. In the next part an improvised routing algorithm known as the extended torus(XTRANC) routing algorithm for NoCs implementation is explained. This algorithm is designed for inner torus mesh networks and provides better performance than usual routing algorithms. It has been implemented using the CONNECT simulator. Then the DART simulator was explored and two important components namely the flitqueue and the traffic generator was designed using this simulator

The Effect Of Hot Spots On The Performance Of Mesh--Based Networks

Direct network performance is affected by different design parameters which include number of virtual channels, number of ports, routing algorithm, switching technique, deadlock handling technique, packet size, and buffer size. Another factor that affects network performance is the traffic pattern. In this thesis, we study the effect of hotspot traffic on system performance. Specifically, we study the effect of hotspot factor, hotspot number, and hot spot location on the performance of mesh-based networks. Simulations are run on two network topologies, both the mesh and torus. We pay more attention to meshes because they are widely used in commercial machines. Comparisons between oblivious wormhole switching and chaotic packet switching are reported. Overall packet switching proved to be more efficient in terms of throughput when compared to wormhole switching. In the case of uniform random traffic, it is shown that the differences between chaotic and oblivious routing are indistinguishable. Networks with low number of hotspots show better performance. As the number of hotspots increases network latency tends to increase. It is shown that when the hotspot factor increases, performance of packet switching is better than that of wormhole switching. It is also shown that the location of hotspots affects network performance particularly with the oblivious routers since their achieved latencies proved to be more vulnerable to changes in the hotspot location. It is also shown that the smaller the size of the network the earlier network saturation occurs. Further, it is shown that the chaos router’s adaptivity is useful in this case. Finally, for tori, performance is not greatly affected by hotspot presence. This is mostly due to the symmetric nature of tori