Moisture computing-based internet of vehicles (IoV) architecture for smart cities

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches

Machine Learning for Next-generation Content Delivery Networks: Deployment, Content Placement, and Performance Management

With the explosive demands for data and the growth in mobile users, content delivery networks (CDNs) are facing ever-increasing challenges to meet end-users quality-of-experience requirements, ensure scalability and remain cost-effective. These challenges encourage CDN providers to seek a solution by considering the new technologies available in today’s computer network domain. Network Function Virtualization (NFV) is a relatively new network service deployment technology used in computer networks. It can reduce capital and operational costs while yielding flexibility and scalability for network operators. Thanks to the NFV, the network functions that previously could be offered only by specific hardware appliances can now run as Virtualized Network Functions (VNF) on commodity servers or switches. Moreover, a network service can be flexibly deployed by a chain of VNFs, a structure known as the VNF Forwarding Graph or VNF-FG. Considering these advantages, the next-generation CDN will be deployed using NFV infrastructure. However, using NFV for service deployment is challenging as resource allocation in a shared infrastructure is not easy. Moreover, the integration of other paradigms (e.g., edge computing and vehicular network) into CDN will compound the complexity of content placement and performance management for the next-generation CDNs. In this regard, due to their impacts on final service and end-user perceived quality, the challenges in service deployment, content placement, and performance management should be addressed carefully. In this thesis, advanced machine learning methods are utilized to provide algorithmic solutions for the abovementioned challenges of the next generation CDNs. Regarding the challenges in the deployment of the next-generation CDNs, we propose two deep reinforcement learning-based methods addressing the joint problems of VNF-FG’s composition and embedding, as well as function scaling and topology adaptation. As for content placement challenges, a deep reinforcement learning-based approach for content migration in an edge-based CDN with vehicular nodes is proposed. The proposed approach takes advantage of the available caching resources in the proximity of the full local caches and efficiently migrates contents at the edge of the network. Moreover, for managing the performance quality of an operating CDN, an unsupervised machine learning anomaly detection method is provided. The proposed method uses clustering to enable easier performance analysis for next-generation CDNs. Each proposed method in this thesis is evaluated by comparison to the state-of-the-art approaches. Moreover, when applicable, the optimality gaps of the proposed methods are investigated as well

Data Processing Delay Optimization in Mobile Edge Computing

With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing