Dependency-Aware Dynamic Task Scheduling in Mobile-Edge Computing

Ma, Tao; Wu, Tao; Chang, Chao An; Fang, Yang; Wang, Huaixi

doi:10.1109/msn50589.2020.00134

Cited by 12 publications

(2 citation statements)

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“…One approach is to use heuristic algorithms to optimize task scheduling and offloading decisions while taking into account the limited computing power of the edge server. For example, reference [5] proposed a heuristic algorithm to schedule dynamic tasks with dependencies, while considering the computing power of the edge server. Reference [6] partitioned tasks into sub-tasks and used an analytical offloading decision for each sub-task to optimize the critical path of a weighted directed acyclic graph.…”

Section: Related Workmentioning

confidence: 99%

“…In reference [4], a study was conducted on the joint exploration of user behavior characteristics and MEC server pricing strategies, examining their interaction and impact on determining the optimal user data offloading strategy. Heuristic algorithms have been widely used to alleviate such problems [5]- [11], but they may fall into a local optimum and require frequent recalculations when the wireless channel changes. Deep neural networks (DNNs) have shown potential to yield better results.…”

Section: Introductionmentioning

confidence: 99%

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Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing

Fang,

Qu,

Chen

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation of the computational power of mobile devices (MDs) by offloading computationally intensive tasks to resource-rich edge nodes. This paper discusses the decision-making process for task offloading and resource allocation among multiple mobile devices connected to a base station. The primary objective is to minimize the time taken to complete tasks while simultaneously reducing energy consumption on the device under a time-varying wireless fading channel. This objective is formulated as an energy-efficiency cost (EEC) minimization problem, which cannot be solved by conventional methods. To address this challenge, we propose a dynamic offloading decision algorithm of dependent tasks (DODA-DT) that adjusts local task execution based on edge node status. The proposed algorithm facilitates fair competition among all devices for edge resources. Additionally, we use a deep reinforcement learning (DRL) algorithm based on an actor-critic learning structure to train the system to quickly identify nearoptimal solutions. Numerical simulations demonstrate that the proposed algorithm effectively reduces the total cost of the task in comparison to previous algorithms.

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Section: Related Workmentioning

confidence: 99%