Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability

The fifth generation (5G) mobile telecommunication network is expected to support Multi- Access Edge Computing (MEC), which intends to distribute computation tasks and services from the central cloud to the edge clouds. Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services, the current mobile network security architecture should enable a more decentralized approach for authentication and authorization processes. This paper proposes a novel decentralized authentication architecture that supports flexible and low-cost local authentication with the awareness of context information of network elements such as user equipment and virtual network functions. Based on a Markov model for backhaul link quality, as well as a random walk mobility model with mixed mobility classes and traffic scenarios, numerical simulations have demonstrated that the proposed approach is able to achieve a flexible balance between the network operating cost and the MEC reliability.Comment: Accepted by IEEE Access on Feb. 02, 201

An optimization model for the US Air-Traffic System

A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function

Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area

Bayesian Network-Based framework for cost-implication assessment of Road Traffic collisions

Abstract: Investigating the cost-implications of road traffic collision factors is an important endeavour that has a direct impact on the economy, transport policies, cities and nations around the world. A Bayesian network framework model was developed using real-life road traffic collision data and expert knowledge to assess the cost of road traffic collisions. Findings of this study suggest that the framework is a promising approach for assessing the cost-implications associated with road traffic collisions. Moreover, adopting this framework with other computational intelligence approaches would have a positive impact towards achieving the Sustainable Development Goals in terms of road safety

Economic Viability of Software Defined Networking (SDN)

Economical and operational facets of networks drive the necessity for significant changes towards fundamentals of networking architectures. Recently, the momentum of programmable networking attempts illustrates the significance of economic aspects of network technologies. Software Defined Networking (SDN) has got the attention of researchers from both academia and industry as a means to decrease network costs and generate revenue for service providers due to features it promises in networking. In this article, we investigate how programmable network architectures, i.e. SDN technology, affect the network economics compared to traditional network architectures, i.e. MPLS technology. We define two metrics, Unit Service Cost Scalability and Cost-to-Service, to evaluate how SDN architecture performs compared to MPLS architecture. Also, we present mathematical models to calculate certain cost parts of a network. In addition, we compare different popular SDN control plane models, Centralized Control Plane (CCP), Distributed Control Plane (DCP), and Hierarchical Control Plane (HCP), to understand the economic impact of them with regards to the defined metrics. We use video traffic with different patterns for the comparison. This work aims at being a useful primer to providing insights regarding which technology and control plane model are appropriate for a specific service, i.e. video, for network owners to plan their investments

Provider and peer selection in the evolving internet ecosystem

The Internet consists of thousands of autonomous networks connected together to provide end-to-end reachability. Networks of different sizes, and with different functions and business objectives, interact and co-exist in the evolving "Internet Ecosystem". The Internet ecosystem is highly dynamic, experiencing growth (birth of new networks), rewiring (changes in the connectivity of existing networks), as well as deaths (of existing networks). The dynamics of the Internet ecosystem are determined both by external "environmental" factors (such as the state of the global economy or the popularity of new Internet applications) and the complex incentives and objectives of each network. These dynamics have major implications on how the future Internet will look like. How does the Internet evolve? What is the Internet heading towards, in terms of topological, performance, and economic organization? How do given optimization strategies affect the profitability of different networks? How do these strategies affect the Internet in terms of topology, economics, and performance? In this thesis, we take some steps towards answering the above questions using a combination of measurement and modeling approaches. We first study the evolution of the Autonomous System (AS) topology over the last decade. In particular, we classify ASes and inter-AS links according to their business function, and study separately their evolution over the last 10 years. Next, we focus on enterprise customers and content providers at the edge of the Internet, and propose algorithms for a stub network to choose its upstream providers to maximize its utility (either monetary cost, reliability or performance). Third, we develop a model for interdomain network formation, incorporating the effects of economics, geography, and the provider/peer selections strategies of different types of networks. We use this model to examine the "outcome" of these strategies, in terms of the topology, economics and performance of the resulting internetwork. We also investigate the effect of external factors, such as the nature of the interdomain traffic matrix, customer preferences in provider selection, and pricing/cost structures. Finally, we focus on a recent trend due to the increasing amount of traffic flowing from content providers (who generate content), to access providers (who serve end users). This has led to a tussle between content providers and access providers, who have threatened to prioritize certain types of traffic, or charge content providers directly -- strategies that are viewed as violations of "network neutrality". In our work, we evaluate various pricing and connection strategies that access providers can use to remain profitable without violating network neutrality.Ph.D.Committee Chair: Dovrolis, Constantine; Committee Member: Ammar, Mostafa; Committee Member: Feamster, Nick; Committee Member: Willinger, Walter; Committee Member: Zegura, Elle

Optimizing total cost of ownership (TCO) for 5G multi-tenant mobile backhaul (MBH) optical transport networks

Legacy network elements are reaching end-of-life and packet-based transport networks are not efficiently optimized. In particular, high density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile BackHaul (MBH) networks with flat revenue generation. Thus, the goal of this dissertation is to provide an efficient physical network planning mechanism and an optimized resource engineering tool in order to reduce the Total Cost of Ownership (TCO) and increase the generated revenues. This will help Service Providers (SPs) and Mobile Network Operators (MNOs) to improve their network scalability and maintain positive Project Profit Margins (PPM). In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to design and migrate to a scalable and reliable MBH network in an optimal cost?, ii) how to control the deployment and activation of the network resources in such MBH based on required traffic demand in an efficient and cost-effective way?, and iii) how to enhance the resource sharing in such network and maximize the profit margins in an efficient way? As part of our contributions to address the first issue highlighted above and to plan the MBH with reduced network TCO and improved scalability, we propose a comprehensive migration plan towards an End-to-End Integrated-Optical-Packet-Network (E2-IOPN) for SP optical transport networks. We review various empirical challenges faced by a real SP during the transformation process towards E2-IOPN as well as the implementation of an as-built plan and a high-level design (HLD) for migrating towards lower cost-per-bit GPON, MPLS-TP, OTN and next-generation DWDM technologies. Then, we propose a longer-term strategy based on SDN and NFV approach that will offer rapid end-to-end service provisioning with costefficient centralized network control. We define CapEx and OpEx cost models and drive a cost comparative study that shows the benefit and financial impact of introducing new low-cost packet-based technologies to carry traffic from legacy and new services. To address the second issue, we first introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to more precisely define MBH zones within a geographical area and more accurately calculate required traffic demands and related MBH infrastructure. In order to optimize the deployment and activation of the network resources in the MBH in an efficient and cost-effective way, we propose a novel method called BackHauling-as-a-Service (BHaaS) for network planning and Total Cost of Ownership (TCO) analysis based on required traffic demand and a "You-pay-only-for-what-you-use" approach. Furthermore, we enhance BHaaS performance by introducing a more service-aware method called Traffic-Profile-asa- Service (TPaaS) to further drive down the costs based on yearly activated traffic profiles. Results show that BHaaS and TPaaS may enhance by 22% the project benefit compared to traditional TCO model. Finally, we introduce a new cost (CapEx and OpEx) models for 5G multi-tenant Virtualized MBH (V-MBH) as part of our contribution to address the third issue. In fact, in order to enhance the resource sharing and maximize the network profits, we drive a novel pay-as-yougrow and optimization model for the V-MBH called Virtual-Backhaul-as-a-Service (VBaaS). VBaaS can serve as a planning tool to optimize the Project Profit Margin (PPM) while considering the TCO and the yearly generated Return-on-Investment (ROI). We formulate an MNO Pricing Game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Then, we compare CapEx, OpEx, TCO, ROI and PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies. The results of our framework offer SPs and MNOs a more precise estimation of traffic demand, an optimized infrastructure planning and yearly resource deployment as well as an optimized TCO analysis (CapEx and OpEx) with enhanced pricing strategy and generated ROI. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH) methods

Valuing flexibility in the migration to flexible-grid networks

Increasing network demand is expected to put pressure on the available capacity in core networks. Flexible optical networking can now be installed to increase network capacity in light of future traffic demands. However, this technology is still in its infancy and might lack the full functionality that may appear within a few years. When replacing core network equipment, it is therefore important to make the right investment decision between upgrading toward flexible-grid or fixed-grid equipment. This paper researches various installation options using a techno-economic analysis, extended with real option insights, showing the impact of uncertainty and flexibility on the investment decision. By valuing the different options, a correct investment decision can be made

Balancing the Migration of Virtual Network Functions with Replications in Data Centers

The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of the so-called Virtual Network Functions (VNFs). Today, cloud service providers use Virtual Machines (VMs) for the instantiation of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a typical scenario of Service Function Chains (SFCs), many important objectives need to be met simultaneously, such as server load balancing, energy efficiency and service execution time. The well-known \emph{VNF placement} problem requires solutions that often consider \emph{migration} of virtual machines (VMs) to meet this objectives. Ongoing efforts, for instance, are making a strong case for migrations to minimize energy consumption, while showing that attention needs to be paid to the Quality of Service (QoS) due to service interruptions caused by migrations. To balance the server allocation strategies and QoS, we propose using \emph{replications} of VNFs to reduce migrations in DC networks. We propose a Linear Programming (LP) model to study a trade-off between replications, which while beneficial to QoS require additional server resources, and migrations, which while beneficial to server load management can adversely impact the QoS. The results show that, for a given objective, the replications can reduce the number of migrations and can also enable a better server and data center network load balancing