Dynamically Controlling Offloading Thresholds in Fog Systems

Alenizi, Faten; Rana, Omer

doi:10.3390/s21072512

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…PSOS can prematurely converge to suboptimal solutions, particularly in the process of dealing with a sizable number of resources and jobs. [17][18] Dynamic Threshold Job Scheduling (DTJS) improves the utilization of resources and minimizes the time of response in VFC. It is grounded on the dynamic threshold concept, where the threshold value is dynamically set based on resource availability and current load.…”

Section: Jsmentioning

confidence: 99%

A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms

Khan,

Saleem,

Hussain

2024

JISR-C

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Fog computing (FC) is considered one of the smart and effective solutions for service provisioning to the Internet of Everything (IoE) layer. IoE layer means the platforms including homes, vehicles, infrastructures, and alike others. FC supports the IoE layer in a smart and charismatic manner by providing services near the smart devices, from vehicles to other mobilities, and fitting the response and delay time requirements. In the proposed paper, the authors focused on discussing vehicular fog computing (VFC) and how FC supports smart vehicles operating on the IoT layer. In the previous decade, scheduling algorithms are proposed by different scholars such as RTFRS, FCFS, ILP, and others to improve the working and efficacy of VFC. In the proposed paper, an analysis is done on the VFC scheduling algorithms published in the last three years in accordance with areas such as traditional, meta-heuristic, deep reinforcement learning, fuzzy logic, heuristic, and integer linear programming. The analysis is done to examine the areas of scheduling such as task scheduling, resource allocation, and others on which the existing solutions are working. This helps in inspecting the gap across which in future further work can be done.

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Section: Jsmentioning

confidence: 99%

A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms

Khan,

Saleem,

Hussain

2024

JISR-C

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“…In the fog-based architecture proposed in [45], the fog layer is an ad hoc network. If the queuing waiting time at any fog node exceeds an offloading threshold value, the fog node sends an offloading request to the best neighboring fog node with the minimum value for the summation of queuing and propagation delays.…”

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%

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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“…As a result, these applications need high computing resources for excellent processing [2][3][4]. Computational offloading enables tasks to be shared between IoT devices, fog nodes, and cloud servers [5]. Nowadays, the computation offloading IoV tasks has been developed efficiently like routing [6,7] based on different optimization techniques that have been proposed [8,9].…”

Section: Introductionmentioning

confidence: 99%

Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

Khadir¹,

Seno²,

Dhahir³

et al. 2023

Computer Systems Science and Engineering

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Fog computing became a traditional OffLad Destination (OLD) to compute the offloaded tasks of the Internet of Vehicles (IoV). Nevertheless, the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud. Thus, the IoV will incur additional offloading costs. In this paper, we propose a new offloading scheme by utilizing RoadSide Parked Vehicles (RSPV) as an alternative OLD for IoV. The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud. Finally, a performance evaluation of the proposed scheme has been presented and discussed with other benchmark offloading schemes.

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Dynamically Controlling Offloading Thresholds in Fog Systems

Cited by 10 publications

References 38 publications

A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms

A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms

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

Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

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