Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

Sun, Lu; Wan, Liangtian; Wang, Jiashuai; Lin, Lin; Gen, Mitsuo

doi:10.1109/tits.2022.3224320

Cited by 21 publications

(11 citation statements)

References 32 publications

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“…As a further optimization, network-aware resource management strategies integrate data center topology information and/or application characteristics. [32]- [35] focus on Integer (Non) Linear Programming (ILP/INLP) models to find the optimal allocation scheme based on an optimization objective. Although able to identify optimal placements, these solutions cannot find a feasible solution within an acceptable time, thus limiting their applicability in production environments.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Castellano,

Galantino,

Risso

et al. 2024

IEEE Open J. Commun. Soc.

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The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for centralized data centers and are ineffective for locating microservices in geographically-distributed edge-like infrastructures. This paper presents Phare, a decentralized scheduling algorithm designed to optimize the placement of microservices by satisfying their computing and communication demands while minimizing deployment costs. Phare employs a heuristic-based approach to solve the NP-Hard scheduling problem, prioritizing the microservices with the more stringent requirements and placing them on the most convenient computing facilities, based on the concept of affinity, contributing to the field by providing a more holistic approach to resource scheduling in edge computing. We validate our approach against Firmament, the state-of-the-art workload scheduling algorithm for component-based applications, on simulated edge infrastructures with hundreds of clusters. Phare achieves up to a 10× reduction in terms of deployment costs compared to Firmament while providing a much lower scheduling latency.

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Section: Related Workmentioning

confidence: 99%

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Castellano,

Galantino,

Risso

et al. 2024

IEEE Open J. Commun. Soc.

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“…Q. Hou et al [37] constructed a two-timescale structure to maximize the minimum average user rate while ensuring fairness. L. Sun et al [38] studied the shortest user requirement response guarantee problem in a UAV-enabled MEC system with the consideration of computing task priority, with the goal of minimizing the maximum stochastic processing time expectation for all tasks. To tackle the problem, a Markovnetwork-based cooperative evolutionary algorithm was devised.…”

Section: Joint Optimization Of Resource Allocation and Uav Trajectory...mentioning

confidence: 99%

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

He,

Liang

2024

Electronics

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The multiple-unmanned-aerial-vehicle (multi-UAV) mobile edge network is a promising networking paradigm that uses multiple resource-limited and trajectory-planned unmanned aerial vehicles (UAVs) as edge servers, upon which on-demand virtual network functions (VNFs) are deployed to provide low-delay virtualized network services for the requests of ground users (GUs), who often move randomly and have difficulty accessing the Internet. However, VNF deployment and UAV trajectory planning are both typical NP-complete problems, and the two operations have a strong coupling effect: they affect each other. Achieving optimal virtualized service provision (i.e., maximizing the number of accepted GU requests under a given period T while minimizing the energy consumption and the cost of accepting the requests in all UAVs) is a challenging issue. In this paper, we propose an improved online deep reinforcement learning (DRL) scheme to tackle this issue. First, we formulate the joint optimization of the two operations as a nonconvex mixed-integer nonlinear programming problem, which can be viewed as a sequence of one-frame joint VNF deployment and UAV-trajectory-planning optimization subproblems. Second, we propose an online DRL based on jointly optimizing discrete (VNF deployment) and continuous (UAV trajectory planning) actions to solve each subproblem, whose key idea is establishing and achieving the coupled influence of discrete and continuous actions. Finally, we evaluate the proposed scheme through extensive simulations, and the results demonstrate its effectiveness.

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“…Object detection is one of the research hotspots in the field of computer vision, and its main tasks include recognizing the category of the object as well as obtaining the location information [1]. In recent years, intelligent UAV technology has been developing rapidly [2,3], and UAV equipped with high-definition video equipment have excellent data acquisition ability, which is not affected by terrain and other factors, and can collect data in complex environments to provide the relevant personnel with the latest data information [4], and the object detection technology is the key for UAV to be applied in many scenarios, such as agricultural monitoring [5,6], traffic monitoring [7,8], search and rescue [9], road monitoring [10], etc., and efficient and accurate image processing capability is indispensable.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale feature fusion and lightweight decoupled Head in small object detection

Lv,

Zhang,

Yuan

et al. 2024

Preprint

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Unmanned aerial vehicle (UAV) equipped with high-definition cameras can quickly acquire image data and are widely applied, however, the task suffers from issues such as smaller objects, large scale transformations, dense objects, and occlusion. To address the above issues, LMS-YOLOv7 is proposed by combining multi-scale feature fusion and lightweight decoupled Head. Firstly, we propose the Skipping Fusion Efficient Layer Aggregation Network (SF-ELAN), in which a cross-layer fusion strategy is used to segment, extract and fuse the features in the channel to enhance the feature representation capability. Secondly, based on the multi-scale feature fusion approach, the Multi-scale Cross Stage Partial (MS-CSP) is proposed to fuse deep and shallow information to avoid losing features. In addition, based on our redesigned LightGSConv, the lightweight decoupled detection Head (LD-Head) is proposed, which decouples the localization information of the feature map from the classification information while keeping the detection head lightweight. Extensive experiments on VisDrone and WiderPer-son demonstrate that the proposed method exhibits higher perception of small objects, and the mAP@.5 metric of our method on the VisDrone improves by 3.6% over the baseline method, and our method achieves the highest accuracy on the WiderPerson. The code for the proposed method is publicly available at GitHub.

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Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

Cited by 21 publications

References 32 publications

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

Multi-scale feature fusion and lightweight decoupled Head in small object detection

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