Adaptive Q-learning-supported Resource Allocation Model in Vehicular Fogs

Urban computing has become a significant driver in supporting the delivery and sharing of services, being a strong ally to intelligent transportation. Smart vehicles present computing and communication capabilities that allow them to enable many autonomous vehicular safety and infotainment applications. Vehicular Cloud Computing (VCC) has already proven to be a technology shifting paradigm harnessing the computation resources from on board units from vehicles to form clustered computing units to solve real world computing problems. However, with the rise of vehicular application use and intermittent network conditions, VCC exhibits many drawbacks. Vehicular Fog computing appears as a new paradigm in enabling and facilitating efficient service and resource sharing in urban environments. Several vehicular resource management works have attempted to deal with the highly dynamic vehicular environment following diverse approaches, e.g. MDP, SMDP, and policy-based greedy techniques. However, the high vehicular mobility causes several challenges compromising consistency, efficiency, and quality of service. RL-enabled adaptive vehicular Fogs can deal with the mobility for properly distributing load and resources over Fogs. Thus, we propose a mobility-based cloudlet dwell time estimation method for accurately estimating vehicular resources in a Fog. Leveraging the CDT estimation model, we devise an adaptive and highly dynamic resource allocation model using mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy

Reliability-optimal cooperative communication and computing in connected vehicle systems

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions

Sporadic cloud-based mobile augmentation on the top of a virtualization layer: a case study of collaborative downloads in VANETs

Current approaches to Cloud-based Mobile Augmentation (CMA) leverage (cloud-based) resources to meet the requirements of rich mobile applications, so that a terminal (the so-called application node or AppN) can borrow resources lent by a set of collaborator nodes (CNs). In the most sophisticated approaches proposed for vehicular scenarios, the collaborators are nearby vehicles that must remain together near the application node because the augmentation service is interrupted when they move apart. This leads to disruption in the execution of the applications and consequently impoverishes the mobile users’ experience. This paper describes a CMA approach that is able to restore the augmentation service transparently when AppNs and CNs separate. The functioning is illustrated by a NaaS model where the AppNs access web contents that are collaboratively downloaded by a set of CNs, exploiting both roadside units and opportunistic networking. The performance of the resulting approach has been evaluated via simulations, achieving promising results in terms of number of downloads, average download times, and network overheadMinisterio de Educación y Ciencia | Ref. TIN2017-87604-

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced