Dynamic Resource Allocation Model for Distribution Operations using SDN

In vehicular ad-hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, a big storage and high computation platform is needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with resource allocation problem assisted by cloud computation. By integration of EC and software defined networking (SDN), this study provides a new software defined networking edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multi-agent RL-based approach using experience reply. The proposed algorithm stores the users’ communication information and the network tracks’ state in real-time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study

Dynamic Resource Allocation Model for Distribution Operations using SDN

In vehicular ad-hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, a big storage and high computation platform is needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with resource allocation problem assisted by cloud computation. By integration of EC and software defined networking (SDN), this study provides a new software defined networking edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multi-agent RL-based approach using experience reply. The proposed algorithm stores the users’ communication information and the network tracks’ state in realtime. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case stud

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing

Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments

D2D-V2X-SDN: Taxonomy and Architecture towards 5G Mobile Communication System

In the era of information society and 5G networks, cars are extremely important mobile information carriers. In order to meet the needs of multi-scenario business requirements such as vehicle assisted driving and in-vehicle entertainment, cars need to interact with the outside world. This interconnection and data transmission process is usually called vehicular communication (V2X, Vehicle-to-Everything). Device-to-device (D2D) communication not only has partial nature of communication, but also alleviate the current problem of spectrum scarcity of resources. The application of D2D communication in V2X can meet the requirements of high reliability and low latency, but resource reuse also brings interference. Software-defined networking (SDN) provides an optimal solution for interoperability and flexibility between the V2X and D2D communication. This paper reviews the integration of D2D and V2X communication from the perspective of SDN. The state-of-the-art and architectures of D2D-V2X were discussed. The similarity, characteristics, routing control, location management, patch scheduling and recovery is described. The integrated architecture reviewed in this paper can solve the problems of routing management, interference management and mobile management. It also overcome the disconnection problem between the D2D-V2X in terms of SDN and provides some effective solutions.- Qatar National Research Fund (QNRF) - [UREP27-020-1-003]. - Ministry of Higher Education, Malaysia (MOHE) - [FRGS/1/2018/ICT02/UKM/02/6]. - National Research Foundation of Korea (NRF) - [2019R1C1C1007277]. - Taif University (TU)- [TURSP-2020/260]