Efficient fog-assisted heterogeneous data services in software defined VANETs

Xiao, Ke; Li, Kai; Xu, Xincao; Zhou, Yi; Feng, Liang

doi:10.1007/s12652-019-01507-8

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…In [42], the authors proposed fog-assisted data services for n a heterogeneous vehicular communication environment. Four communication types are permitted between network nodes: device-tocloud, device-to-fog, fog-to-fog, and device-to-device.…”

Section: Related Workmentioning

confidence: 99%

“…A second offloading was introduced as a dynamic solution that allowed fog nodes to exit the overloading state by redirecting a portion of the workload to other fog nodes. To propose an efficient offloading service, four main questions should be answered: How is the offloading node chosen [34][35][36][37][38][39][40][41][42][43][44][45][46][47], how is the number of offloading nodes decided [37], what tasks are eligible for offloading [35] and how is the number of offloaded tasks determined [38]. The proposed dynamic offloading service attempts to address these questions.…”

Section: Introductionmentioning

confidence: 99%

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Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility of offloading a task, for the second time, to a fog or a cloud node depends to a great extent on task importance, latency constraints, and required resources. This paper presents a dynamic service that determines which tasks can endure a second offloading. The task type, latency constraints, and amount of required resources are used to select the offloading destination node. This study proposes three heuristic offloading algorithms. Each algorithm targets a specific task type. An overloaded fog node can only issue one offloading request to execute one of these algorithms according to the task offloading priority. Offloading requests are sent to a Software Defined Networking (SDN) controller. The fog node and controller determine the number of offloaded tasks. Simulation results show that the average time required to select offloading nodes was improved by 33% when compared to the dynamic fog-to-fog offloading algorithm. The distribution of workload converges to a uniform distribution when offloading latency-sensitive nonurgent tasks. The lowest offloading priority is assigned to latency-sensitive tasks with hard deadlines. At least 70% of these tasks are offloaded to fog nodes that are one to three hops away from the overloaded node.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Alshathri¹,

Hassan²,

Chelloug³

2023

Computers, Materials &Amp; Continua

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“…Due to the high-speed movement of vehicles on highways, network connectivity changes frequently, and it is a challenging task for multi-hop data dissemination in VANETs [115]. Thus, packets cannot reach the destination within the period [116]. Hence, the packet flow is disturbed, resulting in high latency and routing overhead.…”

Section: Systems Under Testmentioning

confidence: 99%

Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey

et al. 2023

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Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous.

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“…Xiao et al's paper entitled "Efficient fog-assisted heterogeneous data services in software defined VANETs" (Xiao et al 2019) focuses on the low-performance problem of data services in heterogenous vehicular ad hoc networks (VANETs). To enhance the performance, this paper presents a novel system architecture integrated with software defined network, fog computing and a dedicated algorithm for cooperative data services.…”

Section: Contributions Of This Special Issuementioning

confidence: 99%