Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

Cheng, Jun; Guan, Donghai

doi:10.1186/s13638-021-01984-6

Cited by 17 publications

(8 citation statements)

References 41 publications

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“…They have not considered optimization techniques for decision-making. In [21], the authors proposed the particle swarm optimization for taking the decision of offloading the task into the MEC server. Here they have prepared a mathematical model for the computation of the cost during the offloading.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment

et al. 2023

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With the technological evolution of mobile devices, 5G and 6G communication and users' demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make suitable offloading decision. The proposed approach utilizes Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity ofthe-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show the reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.

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Section: Related Workmentioning

confidence: 99%

A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment

et al. 2023

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“…We can assess system performance from threedimensional viewpoints, but this poses a significant challenge in terms of system optimization. We examine a weighted performance factor based on delay, energy consumption, and price to simplify the measurement of system performance and the associated optimization [16]. The weighted factor λ turns the utility of the system of multi-objective optimization problem system utility -or called social welfare- [38][39] into a linearly weighted objective function [40].…”

Section: A Joint Performance Metricsmentioning

confidence: 99%

“…The problem is solved by using submodular theory to achieve Nash equilibrium. However, a task-offloading decision strategy is proposed for the MEC Internet of Vehicles (IoV) is proposed in [16] using particle swarm optimization to choose the best offloading technique. The authors established a mathematical model to calculate the cost of computation offloading for MEC, and then particle swarm optimization was used to select the best offloading strategy.…”

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confidence: 99%

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Baktayan

Al-Baltah

Ghani

2022

JCOMSS

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In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delaysensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26% and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

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“…As a result, data processing in the cloud will eventually require increased communication activities via wide-area networks (WANs). This increases traffic in the network, the probability of tasks failures, and evidently results in relatively high response times [3,4]. Mobile Edge Computing (MEC), on the other hand, has already been adopted as a middle layer between the Cloud layer and the Sensing device layer, since various resources (e.g., computation and storage) could be utilized through LAN (Local Area Network) in MEC architecture.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

et al. 2022

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Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and inference, have become more prevalent. Such tasks require data and resources to be executed at the network edge, while transferring data to Cloud servers negatively affects expected response times and quality of service (QoS). In this paper, we study certain computational offloading techniques in autonomous computing nodes (ANs) at the edge. ANs are distinguished by limited resources that are subject to a variety of constraints that can be violated when executing analytical tasks. In this context, we contribute a task-management mechanism based on approximate fuzzy inference over the popularity of tasks and the percentage of overlapping between the data required by a data-driven task and data available at each AN. Data-driven tasks’ popularity and data availability are fed into a novel two-stages Fuzzy Logic (FL) inference system that determines the probability of either executing tasks locally, offloading them to peer ANs or offloading to Cloud. We showcase that our mechanism efficiently derives such probability per each task, which consequently leads to efficient uncertainty management and optimal actions compared to benchmark models.

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Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

Cited by 17 publications

References 41 publications

A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment

A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

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