A Multi-Task Oriented Framework for Mobile Computation Offloading

Lu, Junyu; Li, Qiang; Guo, Bing; Li, Jie; Shen, Yan; Li, Gongliang; Su, Hong

doi:10.1109/tcc.2019.2952346

Cited by 22 publications

(5 citation statements)

References 25 publications

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“…• Local: it is an offloading scheme without computation support from MEC [36]. Each vehicle in this scheme needs to execute applications by its computing capability and cannot offload its tasks to any RSUs.…”

Section: Approaches For Comparisonmentioning

confidence: 99%

MESON: A Mobility-Aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

Zhao

Zhang

Wan³

et al. 2024

IEEE Trans. on Mobile Comput.

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Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because vehicles with time-varying trajectories need to process massive data with high complexity and diversity. In this paper, a VEC-based computation offloading model is developed with consideration of data dependency of tasks. The minimization of the average response time and average energy consumption of the system is defined as a combinatorial optimization problem. To solve this problem, we propose a Mobility-aware dependent task offloading (MESON) Scheme for urban VEC and develop a DRL-based algorithm to train the offloading strategy. To improve the training efficiency, a vehicle mobility detection algorithm is further designed to detect the communication time between vehicles and RSUs. In this way, MESON can avoid unreasonable decisions by lowering the size of the action space. Moreover, to improve the system stability and the offloading successful rate, we design a task priority determination scheme to prioritize the tasks in the waiting queue. The experimental results show that MESON is superior compared to other task offloading schemes in terms of the average response time, average system energy consumption, and offloading successful rate.

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Section: Approaches For Comparisonmentioning

confidence: 99%

MESON: A Mobility-Aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

Zhao

Zhang

Wan³

et al. 2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Therefore, offloading schemes that combine delay demand and energy consumption demand should be considered. Lu et al [30] designed a multitask-offloading policy that could handle dense offloading requests from various mobile devices to optimize the overall execution delay and energy consumption. Wang et al [31] developed an efficient multi-objective evolutionary algorithm to solve the problems of minimizing the response time, minimizing the energy consumption, and minimizing the cost.…”

Section: Related Workmentioning

confidence: 99%

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

et al. 2023

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This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

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“…In addition, for the multiobjective offloading problem, [28] proposed an improved decomposition-based multi-objective evolutionary algorithm that uses a delay-based execution position initialization method for the population initialization problem, and the results show that it outperforms the heuristic algorithm in terms of convergence and diversity of the obtained non-dominated solutions. For intensive task offloading, [29] used a Lyapunov-based elastic parallel search algorithm to solve the proposed energy and delay-based optimization objectives.…”

Section: Related Workmentioning

confidence: 99%

A Vehicle-Assisted Computation Offloading Algorithm Based on Proximal Policy Optimization in Vehicle Edge Networks

Chen

Zeng

et al. 2022

Mobile Netw Appl

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With the continuous development of the Internet of Vehicles(IoV), Vehicle Edge Computing(VEC) has become a key technology for computational resource scheduling, but more and more smart devices are connected to the internet, which makes it difficult for traditional Vehicle Edge Networks(VEN) to deal with tasks in time. In this paper, in order to cope with the challenges of the large number of devices accessing the internet, we propose a vehicle-assisted computation offloading algorithm based on proximal policy optimization(VCOPPO) for User Equipment(UE) tasks, and it combines dynamic parked vehicles incentives mechanism and computational resource allocation strategy by using road vehicles and parked vehicles as edge servers. Firstly, a non-convex optimization problem combining VEN utility and task processing delay is formulated, subject to the constraints of the residual energy and the transmission rate of the task. Secondly, the proposed VCOPPO is used to solve the formulated non-convex optimization problem, and we use stochastic policy to obtain the optimal computation offloading decisions and resource allocation schemes. Finally, the experimental results have shown that the proposed VCOPPO has an excellent performance in network reward and task processing delay respectively, and it can effectively schedule and allocate computational resources. Compared with using Dueling Deep Q Network(Dueling DQN), Deep Q Network(DQN) and Q-learning methods, the proposed VCOPPO improves the network reward by 31%, 18% and 91%, reduces the delay in task processing by 78%, 63% and 74%, respectively.

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A Multi-Task Oriented Framework for Mobile Computation Offloading

Cited by 22 publications

References 25 publications

MESON: A Mobility-Aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

MESON: A Mobility-Aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

A Vehicle-Assisted Computation Offloading Algorithm Based on Proximal Policy Optimization in Vehicle Edge Networks

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