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

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study

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

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

Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches

In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT). However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation

Edge computing and iot analytics for agile optimization in intelligent transportation systems

[EN] With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.This work was partially supported by the Spanish Ministry of Science (PID2019111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019I-ES01-KA103-062602).Peyman, M.; Copado, PJ.; Tordecilla, RD.; Do C. Martins, L.; Xhafa, F.; Juan-Pérez, ÁA. (2021). Edge computing and iot analytics for agile optimization in intelligent transportation systems. Energies. 14(19):1-26. https://doi.org/10.3390/en14196309126141