DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research

Energy-aware Graph Job Allocation in Software Defined Air-Ground Integrated Vehicular Networks

The software defined air-ground integrated vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted graph job allocation problem in SD-AGV networks, where the computation-intensive jobs carried by UAVs, and the vehicular cloud are modeled as graphs. To map each component of the graph jobs to a feasible vehicle, while achieving the trade-off among minimizing UAVs' job completion time, energy consumption, and the data exchange cost among vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving job structures poses addressing the subgraph isomorphism problem, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the subgraph isomorphisms with low computation complexity. For the latter, we introduce a power allocation algorithm by applying convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.Comment: 14 pages, 7 figure

Traffic Road Congestion System using by the internet of vehicles (IoV)

Traffic problems have increased in modern life due to a huge number of vehicles, big cities, and ignoring the traffic rules. Vehicular ad hoc network (VANET) has improved the traffic system in previous some and plays a vital role in the best traffic control system in big cities. But due to some limitations, it is not enough to control some problems in specific conditions. Now a day invention of new technologies of the Internet of Things (IoT) is used for collaboratively and efficiently performing tasks. This technology was also introduced in the transportation system which makes it an intelligent transportation system (ITS), this is called the Internet of vehicles (IOV). We will elaborate on traffic problems in the traditional system and elaborate on the benefits, enhancements, and reasons to better IOV by Systematic Literature Review (SLR). This technique will be implemented by targeting needed papers through many search phrases. A systematic literature review is used for 121 articles between 2014 and 2023. The IoV technologies and tools are required to create the IoV and resolve some traffic rules through SUMO (simulation of urban mobility) which is used for the design and simulation the road traffic. We have tried to contribute to the best model of the traffic control system. This paper will analysis two vehicular congestion control models in term of select the optimized and efficient model and elaborate on the reasons for efficiency by searching the solution SLR based questions. Due to some efficient features, we have suggested the IOV based on vehicular clouds. These efficient features make this model the best and most effective than the traditional model which is a great reason to enhance the network system.Comment: pages 16, figures

Next Generation of SDN in Cloud-Fog for 5G and Beyond-Enabled Applications: Opportunities and Challenges

In recent years, the number of objects connected to the Internet has significantly increased. Increasing the number of connected devices to Internet is transforming today’s Internet of Things (IoT) into massive IoT of future. It is predicted, in a few years, a high communication and computation capacity will be required to meet demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill part of these requirements by providing data rate of up to Terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constrains. On the other hand, next generation of Software Defined Networking (SDN) with emerging Cloud related technologies (e.g., Fog and Edge computing) can play an important role on supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid Cloud-Fog systems to support 5G and beyond-enabled applications