A collaborative task offloading strategy for mobile edge computing in internet of vehicles

Song, Zhiyuan; Ma, Ruijiang; Xie, Yong

doi:10.1109/iaeac50856.2021.9390817

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…To address this issue, it is necessary to introduce horizontal collaboration to complement the vertical collaboration paradigm. Song et al [11] proposed a collaborative offloading strategy based on vehicular networks, where computing tasks can be processed locally, offloaded to roadside units or the cloud center, and also can be offloaded to cooperative vehicles for processing. Simulation results show that this approach can provide sufficient computing resources for vehicles.…”

Section: Related Workmentioning

confidence: 99%

A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory

Qiao-Feng Song,

Jun Wang

et al. 2023

Journal of Computers

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<p>A common approach in existing collaborative edge computing offloading schemes is to partition tasks into independent sub-tasks and offload them to participating servers. However, in practice, these sub-tasks often have dependencies, resulting in waiting time. To address this problem, we propose a collaborative computation offloading scheme based on Stackelberg game theory and graph theory (CCOSGG). First, we introduce a task clustering method based on graph theory, which uses task reconstruction and graph partition algorithm to cluster strongly related sub-tasks into appropriately sized clusters. Second, we use Stackelberg game theory to introduce an incentive mechanism that encourages remote edges to participate in the collaborative offloading. Finally, simulation results demonstrate that the proposed scheme can minimize latency and energy consumption at different network scales.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory

Qiao-Feng Song,

Jun Wang

et al. 2023

Journal of Computers

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“…The transmission rate R wired for RSU wired transmission to the MEC is 100 Gb/s [50]. The calculation capacity of cars and MEC is set to 1.4 Gr/s and 2.8 Gr/s, respectively Song et al [51].…”

Section: Simulation Setupmentioning

confidence: 99%

Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

Wang,

Zhou,

et al. 2023

Front. Phys.

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With the continuous development of the 6G mobile network, computing-intensive and delay-sensitive onboard applications generate task data traffic more frequently. Particularly, when multiple intelligent agents are involved in tasks, limited computational resources cannot meet the new Quality of Service (QoS) requirements. To provide a satisfactory task offloading strategy, combining Multi-Access Edge Computing (MEC) with artificial intelligence has become a potential solution. In this context, we have proposed a task offloading decision mechanism (TODM) based on cooperative game and deep reinforcement learning (DRL). A joint optimization problem is presented to minimize both the overall task processing delay (OTPD) and overall task energy consumption (OTEC). The approach considers task vehicles (TaVs) and service vehicles (SeVs) as participants in a cooperative game, jointly devising offloading strategies to achieve resource optimization. Additionally, a proximate policy optimization (PPO) algorithm is designed to ensure robustness. Simulation experiments confirm the convergence of the proposed algorithm. Compared with benchmark algorithms, the presented scheme effectively reduces delay and energy consumption while ensuring task completion.

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Joint Optimization Task Offloading Strategy for Mobile Edge Computing

Tang

Zhan

Chen

et al. 2021

2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)

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Mobile-edge computing (MEC) has been envisioned as a promising paradigm to meet ever-increasing resource demands of mobile users, prolong battery lives of mobile devices, and shorten request response delays experienced by users. An MEC environment consists of many MEC servers and ubiquitous access points interconnected into an edge cloud network. Mobile users can offload their computing-intensive tasks to one or multiple MEC servers for execution to save their batteries. Due to large numbers of MEC servers deployed in MEC, selecting a subset of servers to serve user tasks while satisfying delay requirements of their users is challenging. In this paper, we formulate a novel delay-energy joint optimization problem through jointly considering the CPU-cycle frequency scheduling at mobile devices, server selection to serve user offloading tasks, and task allocations to the selected servers. To this end, we first formulate the problem as a mixed-integer nonlinear programming, due to the hardness to solve this nonlinear programming, we instead then relax the problem into a nonlinear programming problem that can be solved in polynomial time. We also show how to derive a feasible solution to the original problem from the solution of this relaxed solution. We finally conduct experiments to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising. * W is truncated to a nearest integer W if it is not an integer.

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A collaborative task offloading strategy for mobile edge computing in internet of vehicles

Cited by 8 publications

References 14 publications

A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory

A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory

Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

Joint Optimization Task Offloading Strategy for Mobile Edge Computing

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