Centralized model driven trace route mechanism for TCP/IP routers : Remote traceroute invocation using NETCONF API and YANG data model

During the recent years, utilizing programmable APIs and YANG data model for service configuration and monitoring of TCP/IP open network devices from a centralized network management system as an alternative to SNMP based network management solutions has gained popularity among service providers and network engineers. However, both SNMP and YANG lacks any data model for tracing the routes between different routers inside and outside the network that has not addressed. Having a centralized traceroute tool provides a central troubleshooting point in the network. And rather than having to individually connect to each router terminal, traceroute can be invoked remotely on different routers. And the responses can be collected on the network management system. The aim of this thesis is to develop a centralized traceroute tool called Trace that invokes traceroute CLI tool with a unique syntax from a centralized network management system on a TCP/IP router, traces the hops and BGP AS and measures RTT between a router and specific destination and returns the response back to the network management system. And evaluates the possibility of utilizing this traceroute tool along with YANG based network management solutions. This implementation has shown that YANG based data models enables a unique syntax on the network management system for invoking traceroute command on different TCP/IP devices. This unique syntax can be used to invoke the traceroute CLI command on the routers with the different operating systems. And the evaluation has shown that using NETCONF as an API between the network management system and the network devices, enables the Trace to be utilized in YANG and NETCONF based network management solutions

Centralized model driven trace route mechanism for TCP/IP routers : Remote traceroute invocation using NETCONF API and YANG data model

During the recent years, utilizing programmable APIs and YANG data model for service configuration and monitoring of TCP/IP open network devices from a centralized network management system as an alternative to SNMP based network management solutions has gained popularity among service providers and network engineers. However, both SNMP and YANG lacks any data model for tracing the routes between different routers inside and outside the network that has not addressed. Having a centralized traceroute tool provides a central troubleshooting point in the network. And rather than having to individually connect to each router terminal, traceroute can be invoked remotely on different routers. And the responses can be collected on the network management system. The aim of this thesis is to develop a centralized traceroute tool called Trace that invokes traceroute CLI tool with a unique syntax from a centralized network management system on a TCP/IP router, traces the hops and BGP AS and measures RTT between a router and specific destination and returns the response back to the network management system. And evaluates the possibility of utilizing this traceroute tool along with YANG based network management solutions. This implementation has shown that YANG based data models enables a unique syntax on the network management system for invoking traceroute command on different TCP/IP devices. This unique syntax can be used to invoke the traceroute CLI command on the routers with the different operating systems. And the evaluation has shown that using NETCONF as an API between the network management system and the network devices, enables the Trace to be utilized in YANG and NETCONF based network management solutions

Implementation of a non-rt ric for automation service deployment over 4g small cells based on openairinterface technology

With the continuous evolution of the mobile communications networks, it is important to have virtualized open-source ecosystems such as OpenAirInterface, that can provide low cost networks to the different operators. In this project we aim to develop a centralised radio controller for the management of 4G small cells based on OpenAirInterface technology. This radio controller interacts with the specific solution equivalent to OAI's RT RIC called FlexRAN. The objective of the radio controller is to achieve an automated deployment of 4G services over OAI technology cells, where each service is assigned several dedicated radio resources (RAN slice). Specifically, the aim is to manage the radio resources assigned to the different services dynamically and according to the requirements of the service (QoS, latency, etc.)

Automating network and service configuration using NETCONF and YANG

Network providers are challenged by new requirements for fast and error-free service turn-up. Existing approaches to configuration management such as CLI scripting, device-specific adapters, and entrenched commercial tools are an impediment to meeting these new requirements. Up until recently, there has been no standard way of configuring network devices other then SNMP and SNMP is not optimal for configuration management. The IETF has released NETCONF and YANG which are standards focusing on Configuration management. We have validated that NETCONF and YANG greatly simplify the configuration management of devices and services and still provide good performance. Our performance tests are run in a cloud managing 2000 devices. Our work can help existing vendors and service providers to validate a standardized way to build configuration management solutions.Godkänd; 2011; 20110718 (stewal)</p

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Adaptive and Scalable Controller Placement in Software-Defined Networking

Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets. Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations. To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequency—collectively contributing to elevated standards in SDN deployments