Deep Neural Network Task Partitioning and Offloading for Mobile Edge Computing

Gao, Mingliang; Cui, Wenqi; Gao, Di; Shen, Rujing; Li, Jun; Zhou, Yiqing

doi:10.1109/globecom38437.2019.9013404

Cited by 21 publications

(11 citation statements)

References 15 publications

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“…Refs. [ 6 , 21 , 22 ] also adopt similar strategies in order to identify the best partition points. However, unlike other works using exhaustive searching, ref.…”

Section: Literature Reviewmentioning

confidence: 99%

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Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference

Zhang

Lian

et al. 2023

Sensors

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In order to effectively apply BranchyNet, a DNN with multiple early-exit branches, in edge intelligent applications, one way is to divide and distribute the inference task of a BranchyNet into a group of robots, drones, vehicles, and other intelligent edge devices. Unlike most existing works trying to select a particular branch to partition and deploy, this paper proposes a genetic algorithm (GA)-based online partitioning approach that splits the whole BranchyNet with all its branches. For this purpose, it establishes a new calculation approach based on the weighted average for estimating total execution time of a given BranchyNet and a two-layer chromosome GA by distinguishing partitioning and deployment during the evolution in GA. The experiment results show that the proposed algorithm can not only result in shorter execution time and lower device-average energy cost but also needs less time to obtain an optimal deployment plan. Such short running time enables the proposed algorithm to generate an optimal deployment plan online, which dynamically meets the actual requirements in deploying an intelligent application in the edge.

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“…Refs. [ 6 , 21 , 22 ] also adopt similar strategies in order to identify the best partition points. However, unlike other works using exhaustive searching, ref.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, unlike other works using exhaustive searching, ref. [ 22 ] solves the problem by mixed integer linear programming.…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference

Zhang

Lian

et al. 2023

Sensors

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“…In recent years, optimization methods based on reinforcement learning [17][18][19][20] and artificial intelligence [21,22] have emerged. A reinforcement learning-based online computation offloading approach for block chainempowered mobile edge computing was proposed in [17].…”

Section: Related Workmentioning

confidence: 99%

“…In [18], deep reinforcement learning is first proposed to solve the offloading problem of multiple service nodes for the cluster and multiple dependencies for mobile tasks in large-scale heterogeneous MEC. Gao et al investigated a DNN based MEC scheme considering multiple mobile devices and one MEC server in [19]. A Q-learning based flexible task scheduling with global view (QFTS-GV) scheme is proposed to improve task scheduling success rate, reduce delay, and extend lifetime for the IoT in [20].…”

Section: Related Workmentioning

confidence: 99%

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Liao

Peng

et al. 2021

J Cloud Comp

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With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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“…In addition, they proposed a two-timescale algorithm by applying the Lyapunov stochastic optimization and matching theory to find the optimal UE-server association, task offloading, and resource allocation. Moreover, Gao et al [18] proposed a DNN inference delay prediction model to estimate the processing delays that vary depending on the DNN model partition pattern and then formulated the joint problem as a mixedinteger linear programming (MILP) problem. Meanwhile, He et al [19] formulated a joint optimization problem as a mixed-integer nonlinear programming (MINLP) problem, decomposed the optimization problem into a computing resource allocation (CRA) and a DNN partition deployment (DPD), and then devised low-complexity algorithms using the Markov approximation to find the suboptimal solution in polynomial time.…”

Section: B Computation Offloading Decision Algorithmsmentioning

confidence: 99%

Implementing Practical DNN-Based Object Detection Offloading Decision for Maximizing Detection Performance of Mobile Edge Devices

et al. 2021

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In the last decade, deep neural network (DNN)-based object detection technologies have received significant attention as a promising solution to implement a variety of image understanding and video analysis applications on mobile edge devices. However, the execution of computationally intensive DNN-based object detection workloads in mobile edge devices is insufficient in fulfilling the object detection requirements with high accuracy and low latency, owing to the limited computation capacity. In this paper, we implement and evaluate a DNN-based object detection offloading framework to improve the object detection performance of mobile edge devices by offloading computation-intensive workloads to a remote edge server. However, preliminary experimental results have shown that offloading all object detection workloads of mobile edge devices may lead to worse performance than executing the workloads locally. This degradation is obtained from the inefficient resource utilization in the edge computing architectures, both for the edge server and mobile edge devices. To resolve the aforementioned problem with degradation, we devise a device-aware DNN offloading decision algorithm that is aimed to maximize resource utilization in the edge computing architecture. The proposed algorithm decides whether or not to offload the object detection workloads of edge devices by considering their computing power and network bandwidth, and therefore maximizing their average object detection processing frames per second. Through various experiments conducted in a real-life wireless local area network (WLAN) environment, we verified the effectiveness of the proposed DNN-based object detection offloading framework.INDEX TERMS Deep learning offloading, object detection, wireless edge computing, resource optimization.

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Deep Neural Network Task Partitioning and Offloading for Mobile Edge Computing

Cited by 21 publications

References 15 publications

Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference

Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Implementing Practical DNN-Based Object Detection Offloading Decision for Maximizing Detection Performance of Mobile Edge Devices

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