Joint Offloading and Resource Allocation for Time-Sensitive Multi-Access Edge Computing Network

Yu, Junjie; Zhao, Mingxiong; Li, Wentao; Liu, Di; Yao, Shaowen; Feng, Wei

doi:10.1109/wcnc45663.2020.9120620

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…In Ref. [77], the authors studied fight RSUs carried by UAVs and designed a flight algorithm on the basis of swarm intelligence. Through intelligence calculation for road conduction information, the authors were able to dynamically formulate the processes of task offloading and resource allocation.…”

Section: Referencementioning

confidence: 99%

“…[76] An offloading scheme using TVWS Reduce the cost in VTs and edge servers. [77] A Deep Reinforcement Learning (DRL) method…”

Section: Referencementioning

confidence: 99%

“…In Ref. [77], the authors designed a vehicle EC network architecture, in which the vehicles could act as the EC servers to provide adjacent vehicles with services. Then, considering the delay in computations, the authors proposed a vehicle-assisted offloading scheme.…”

Section: Referencementioning

confidence: 99%

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Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

et al. 2022

Tsinghua Sci. Technol.

149

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The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resourceintensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

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Section: Referencementioning

confidence: 99%

“…[76] An offloading scheme using TVWS Reduce the cost in VTs and edge servers. [77] A Deep Reinforcement Learning (DRL) method…”

Section: Referencementioning

confidence: 99%

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Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

et al. 2022

Tsinghua Sci. Technol.

149

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“…Scope of this paper: Edge computing is an emergent research topic, consisting of many interesting and challenging research problems, like edge caching [30] and computation offloading [31], [32]. Prior review or surveys have profoundly discussed these topics.…”

Section: B Structure Of This Papermentioning

confidence: 99%

Bringing AI To Edge: From Deep Learning's Perspective

Liu,

Kong,

Luo

et al. 2020

Preprint

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Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the development of edge intelligence systems encounters some challenges, and one of these challenges is the computational gap between computation-intensive deep learning algorithms and less-capable edge systems. Due to the computational gap, many edge intelligence systems cannot meet the expected performance requirements. To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search. Although some reviews or surveys have partially covered this large body of literature, we lack a systematic and comprehensive review to discuss all aspects of these deep learning techniques which are critical for edge intelligence implementation. As various and diverse methods which are applicable to edge systems are proposed intensively, a holistic review would enable edge computing engineers and community to know the state-of-the-art deep learning techniques which are instrumental for edge intelligence and to facilitate the development of edge intelligence systems. This paper surveys the representative and latest deep learning techniques that are useful for edge intelligence systems, including hand-crafted models, model compression, hardware-aware neural architecture search and adaptive deep learning models. Finally, based on observations and simple experiments we conducted, we discuss some future directions.

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“…However, the heavy computation workload and storage demand lay a burden on the resource-limited IoT devices. To find a way out of this dilemma, Mobile Edge Computing (MEC) has been envisioned as the paradigm by placing servers with rich services in proximity to IoT devices, which can remarkably improve the quality of experience (QoE) of IoT devices and help to reduce the energy dissipation [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

Zhao

et al. 2020

Preprint

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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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Joint Offloading and Resource Allocation for Time-Sensitive Multi-Access Edge Computing Network

Cited by 18 publications

References 20 publications

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Bringing AI To Edge: From Deep Learning's Perspective

Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

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