Network Routing Using the Network Tasking Order, a Chron Approach

This thesis promotes the use of the network tasking order (NTO), in collaboration with the air tasking order (ATO), to optimize routing in Mobile Ad hoc Networks (MANET). The network topology created by airborne platforms is determined ahead of time and network transitions are calculated offline prior to mission execution. This information is used to run maximum multi-commodity flow algorithms offline to optimize network flow and schedule route changes for each network node. These calculations and timely route modifications increases network efficiency. This increased performance is critical to command and control decision making in the battlefield. One test scenario demonstrates near a 100% success rate when route scheduling and splitting network traffic over an emulated MANET compared to Open Shortest Path First (OSPF) which only achieved around a 71% success rate, and Mesh Made Easy (MME) which achieved about 50% success. Another test scenario demonstrates that the NTO can experience degradation due to schedule delay. Overall, if executed and planned properly, the NTO can significantly improve network Quality of Service (QoS)

Scheduling of routing table calculation schemes in open shortest path first using artificial neural network

Internet topology changes due to events such as router or link goes up and down. Topology changes trigger routing protocol to undergo convergence process which eventually prepares new shortest routes needed for packet delivery. Real-time applications (e.g. VoIP) are increasingly being deployed in internet nowadays and require the routing protocols to have quick convergence times in the range of milliseconds. To speed-up its convergence time and better serve real-time applications, a new routing table calculation scheduling schemes for Interior Gateway Routing Protocol called Open Shortest Path First (OSPF) is proposed in this research. The proposed scheme optimizes the scheduling of OSPF routing table calculations using Artificial Neural Network technique called Generalized Regression Neural Network. The scheme determines the suitable hold time based on three parameters: LSA-inter arrival time, the number of important control message in queue, and the computing utilization of the routers. The GRNN scheme is tested using Scalable Simulation Framework (SSFNet version 2.0) network simulator. Two kind of network topology with several link down scenarios used to test GRNN scheme and existing scheme (fixed hold time scheme). Results shows that GRNN provide faster convergence time compared to the existing scheme

Tree based reliable topology for distributing link state information

Finding paths that satisfy the performance requirements of applications according to link state information in a network is known as the Quality-of- Service (QoS) routing problem and has been extensively studied. However, distributing link state information may introduce a significant protocol overhead on network resources. In this thesis, the issue on how to update link state information efficiently and effectively is investigated. A theoretical framework is presented, and a high performance link state policy that is capable of minimizing the false blocking probability of connections under a given update rate constraint is proposed. Through theoretical analysis, it is shown that the proposed policy outperforms the current state of the art in terms of the update rate and higher scalability and reliability

Fast traffic engineering by gradient descent with learned differentiable routing

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion).This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (˜25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University and by the PL-Grid Infrastructure. Also, this publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.Peer ReviewedPostprint (author's final draft

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

QoS Provisioning in Converged Satellite and Terrestrial Networks: A Survey of the State-of-the-Art

It has been widely acknowledged that future networks will need to provide significantly more capacity than current ones in order to deal with the increasing traffic demands of the users. Particularly in regions where optical fibers are unlikely to be deployed due to economical constraints, this is a major challenge. One option to address this issue is to complement existing narrow-band terrestrial networks with additional satellite connections. Satellites cover huge areas, and recent developments have considerably increased the available capacity while decreasing the cost. However, geostationary satellite links have significantly different link characteristics than most terrestrial links, mainly due to the higher signal propagation time, which often renders them not suitable for delay intolerant traffic. This paper surveys the current state-of-the-art of satellite and terrestrial network convergence. We mainly focus on scenarios in which satellite networks complement existing terrestrial infrastructures, i.e., parallel satellite and terrestrial links exist, in order to provide high bandwidth connections while ideally achieving a similar end user quality-of-experience as in high bandwidth terrestrial networks. Thus, we identify the technical challenges associated with the convergence of satellite and terrestrial networks and analyze the related work. Based on this, we identify four key functional building blocks, which are essential to distribute traffic optimally between the terrestrial and the satellite networks. These are the traffic requirement identification function, the link characteristics identification function, as well as the traffic engineering function and the execution function. Afterwards, we survey current network architectures with respect to these key functional building blocks and perform a gap analysis, which shows that all analyzed network architectures require adaptations to effectively support converged satellite and terrestrial networks. Hence, we conclude by formulating several open research questions with respect to satellite and terrestrial network convergence.This work was supported by the BATS Research Project through the European Union Seventh Framework Programme under Contract 317533