Master of Science

Cloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including faster module development for network functions, more rapid deployment, enhancement of the network on cloud infrastructures, and lowering the overall cost of having a network system. All these benefits can be achieved in NFV by turning physical network functions into Virtual Network Functions (VNFs). However, since this technology is still a new network paradigm, integrating this virtual environment into a legacy environment or even moving all together into NFV reflects on the complexity of adopting the NFV system. Also, a network service could be composed of several components that are provided by different service providers; this also increases the complexity and heterogeneity of the system. We apply abstract architectural modeling to describe and analyze the NFV architecture. We use architectural patterns to build a flexible NFV architecture to build a Reference Architecture (RA) for NFV that describe the system and how it works. RAs are proven to be a powerful solution to abstract complex systems that lacks semantics. Having an RA for NFV helps us understand the system and how it functions. It also helps us to expose the possible vulnerabilities that may lead to threats toward the system. In the future, this RA could be enhanced into SRA by adding misuse and security patterns for it to cover potential threats and vulnerabilities in the system. Our audiences are system designers, system architects, and security professionals who are interested in building a secure NFV system. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Layered structures are commonly used in communication systems, but their roles in decentralized control are not understood well. In the first part of this thesis, a theoretical study of consensus (a typical decentralized control task) in layered structures is conducted. The unique graph topology approach permits explicit characterization of consensus performance based on simple graphical characteristics of MLMG structures. In the second part of this thesis, a generic LEGO test-bed to mimic multi-domain communication with layered structures is described. A search-and-rescue scenario is implemented to demonstrate the use of the test-bed.

No description available.

Today's Network Operation Centers (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network's health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today's networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors' technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this work, we investigate whether such decisions can be taken by Artificial Intelligence instead of collective human intelligence, specifically by Deep-Reinforcement Learning (DRL), which has been shown in computer games to outperform humans. We build an Action Recommendation Engine (ARE) based on RL, train it with expert rules or by letting it explore outcomes by itself, and show that it can learn new and more efficient strategies that outperform expert rules designed by humans by as much as 25%.

This report contains design, architecture and implementation of a system that is used to~monitor services and files in computer networks. The monitoring system is based on~principles of software defined networks. The~first part of the report focuses on the description of~ SDN principles and OnePK platform, which is used for the construction of the monitoring system. The second part of the work consists of the design and implementation of~the~monitoring system as a OnePK application. Finally, the evaluation of the monitoring system is provided.

Master of Science

Master of Engineering

Master of Science

<p>This project is a token bus local area network simulator written in Pascal on an IBM PC compatible. The simulator is written for the Microsoft Windows operating environment and makes use of a graphical user interface for controlling the simulation. The program is object-oriented to make use of the Borland ObjectWindows Windows interface and because of the suitability of object-oriented programming to graphics and simulation applications. All basic token bus network functionalities specified by the Institute Of Electrical And Electronics Engineers (IEEE) 802.4 token bus standard are implemented in the simulation plus an added function to resolve duplicate node addresses. The network nodes and bus are drawn using Windows' color graphics. The state of each node is represented by text as well as the color and style each node is drawn in. The frame being transmitted is shown as large text within the bus object on the screen. The direction of data transfer on the bus is shown graphically as is the current location of the token among the nodes.</p> <p> The user of the simulation has the ability make any node active, inactive, or passive, or to make any node fail. The user may make a node send data to one other node or many other random nodes. The addresses of the nodes may also be changed. The user may pause, step through, or continue the simulation, control the simulation speed, control the error rate of data on the bus, and produce a lost token scenario.</p> / Master of Science