Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach

The UbuntuNet Alliance, a consortium of National Research and Education Networks (NRENs) runs an exclusive data network for education and research in east and southern Africa. Despite a high degree of route redundancy in the Alliance's topology, a large portion of Internet traffic between the NRENs is circuitously routed through Europe. This thesis proposes a performance-based strategy for dynamic ranking of inter-NREN paths to reduce latencies. The thesis makes two contributions: firstly, mapping Africa's inter-NREN topology and quantifying the extent and impact of circuitous routing; and, secondly, a dynamic traffic engineering scheme based on Software Defined Networking (SDN), Locator/Identifier Separation Protocol (LISP) and Reinforcement Learning. To quantify the extent and impact of circuitous routing among Africa's NRENs, active topology discovery was conducted. Traceroute results showed that up to 75% of traffic from African sources to African NRENs went through inter-continental routes and experienced much higher latencies than that of traffic routed within Africa. An efficient mechanism for topology discovery was implemented by incorporating prior knowledge of overlapping paths to minimize redundancy during measurements. Evaluation of the network probing mechanism showed a 47% reduction in packets required to complete measurements. An interactive geospatial topology visualization tool was designed to evaluate how NREN stakeholders could identify routes between NRENs. Usability evaluation showed that users were able to identify routes with an accuracy level of 68%. NRENs are faced with at least three problems to optimize traffic engineering, namely: how to discover alternate end-to-end paths; how to measure and monitor performance of different paths; and how to reconfigure alternate end-to-end paths. This work designed and evaluated a traffic engineering mechanism for dynamic discovery and configuration of alternate inter-NREN paths using SDN, LISP and Reinforcement Learning. A LISP/SDN based traffic engineering mechanism was designed to enable NRENs to dynamically rank alternate gateways. Emulation-based evaluation of the mechanism showed that dynamic path ranking was able to achieve 20% lower latencies compared to the default static path selection. SDN and Reinforcement Learning were used to enable dynamic packet forwarding in a multipath environment, through hop-by-hop ranking of alternate links based on latency and available bandwidth. The solution achieved minimum latencies with significant increases in aggregate throughput compared to static single path packet forwarding. Overall, this thesis provides evidence that integration of LISP, SDN and Reinforcement Learning, as well as ranking and dynamic configuration of paths could help Africa's NRENs to minimise latencies and to achieve better throughputs

A Data Distribution Service in a hierarchical SDN architecture: implementation and evaluation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Software-defined networks (SDNs) have caused a paradigm shift in communication networks as they enable network programmability using either centralized or distributed controllers. With the development of the industry and society, new verticals have emerged, such as Industry 4.0, cooperative sensing and augmented reality. These verticals require network robustness and availability, which forces the use of distributed domains to improve network scalability and resilience. To this aim, this paper proposes a new solution to distribute SDN domains by using Data Distribution Services (DDS). The DDS allows the exchange of network information, synchronization among controllers and auto-discovery. Moreover, it increases the control plane robustness, an important characteristic in 5G networks (e.g., if a controller fails, its resources and devices can be managed by other controllers in a short amount of time as they already know this information). To verify the effectiveness of the DDS, we design a testbed by integrating the DDS in SDN controllers and deploying these controllers in different regions of Spain. The communication among the controllers was evaluated in terms of latency and overhead.Postprint (author's final draft

The Mask: Masking the effects of Edge Nodes being unavailable

The arctic tundra is observed to collect data to be used for climate research. Data can be collected by cyber-physical computers with sensors. However, the arctic tundra has a limited availability of energy. Consequently, the nodes rely on batteries and sleep most of the time to increase the battery-limited operational lifetime. In addition, only a few nodes can expect to be in reach of a back-haul wireless data network. Consequently, the nodes have on-node wireless local area networks to reach nearby neighbor nodes. To increase the availability for remote clients to the data collected by the nodes, a set of shadow nodes are used. These are always on, and always have access to a back-haul network. Data from an edge node on the arctic tundra propagates to the shadow nodes either directly over a back-haul network, or via a neighbor node with a back-haul network. The purpose is to make the data produced by an edge node available to a client even when the edge node sleeps or no network access is available. A statistical analysis is done to characterize the prototype’s behavior under a set of edge-node behaviors. To validate the statistical analysis a prototype system is developed and used in a set of performance-measuring experiments. Experiments are done with 10 to 1,000,000 nodes, different probabilities of nodes being awake, and different probabilities of the back-haul network being available. Edge and shadow nodes are emulated as Go functions and executed on a high-performance computer with thousands of cores. Different wireless networks are emulated albeit in a simplified way. A run-time simulation system is developed to control the prototype and conduct the experiments. The results for the prototype show that if the single synchronization chance is low or the desired time to get the latest data should be minimized, an additional data delivery path should be considered on the edge node’s side. Synchronization via the right neighbor principle adds an extra communication channel which increases the data availability level by 50%-100%, but the resource demand grows by 30% per unit. The time required to get the latest data from edge nodes decreases by a factor of 1.75. The results for the simulation show that the cumulative network throughput of approximately ≈ 2100 MB/s and the Generated Data Amount ≈ 25000 MB/s can be achieved at the cost of ≈ 80 KB RAM per emulated node. The results show that the statistical analysis and the results from the prototype as used by the simulation system match, but the statistical expectation considers a limited range of factors. Statistically derived values can be used as the input for the simulation, where they would be adjusted to get a more comprehensive result. The conclusions are that the Mask provides instant access to data storage for edge nodes. The Mask is fronted to clients which become able to retrieve the data asynchronously, even when edge nodes are offline

Constructing Dynamic Ad-hoc Emergency Networks using Software-Defined Wireless Mesh Networks

Natural disasters and other emergency situations have the potential to destroy a whole network infrastructure needed for communication critical to emergency rescue, evacuation, and initial rehabilitation. Hence, the research community has begun to focus attention on rapid network reconstruction in such emergencies; however, research has tried to create or improve emergency response systems using traditional radio and satellite communications, which face high operation costs and frequent disruptions. This thesis proposes a centralized monitoring and control system to reconstruct ad-hoc networks in emergencies by using software-defined wireless mesh networks (SDWMN). The proposed framework utilizes wireless mesh networks and software-defined networking to provide real-time network monitoring services to restore Internet access in a targeted disaster zone. It dispatches mobile devices including unmanned aerial vehicles and self-driving cars to the most efficient location aggregation to recover impaired network connections by using a new GPS position finder (GPS-PF) algorithm. The algorithm is based on density-based spatial clustering that calculates the best position to deploy one of the mobile devices. The proposed system is evaluated using the common open research emulator to demonstrate its efficiency and high accessibility in emergency situations. The results obtained from the evaluation show that the performance of the emergency communication system is improved considerably with the incorporation of the framework

Scalable Real-time Emulation of 5G Networks with Simu5G

Real-time emulation of 5G networks is highly beneficial for several purposes, such as prototyping or performance evaluation of distributed applications meant to run on 5G networks, research demonstration, evaluation of other technologies (e.g., Multi-access Edge Computing) meant to interoperate with 5G access. In this work, we describe how to use Simu5G, a new end-to-end simulator of 5G networks based on OMNeT++, as a real-time emulator. We describe in detail the modeling choices that allow emulation to scale up without compromising accuracy. We present a thorough evaluation of the Simu5G’s emulation capabilities, showing that networks with hundreds of simulated users and tens of cells can be emulated on a single desktop machine