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Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence [article]

Shuiguang Deng, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar, Albert Y. Zomaya
2020 arXiv   pre-print
Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity.  ...  of building AI models, i.e., model training and inference, on the edge.  ...  For the longterm stochastic optimization problem, several computationally efficient algorithms are developed based on Q-learning. 3) Computation Offloading: Computation offloading can be considered as  ... 
arXiv:1909.00560v2 fatcat:jmg3fyagazdzfbn7duiqihagea

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning [article]

Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen
2019 arXiv   pre-print
And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of  ...  In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement  ...  DATA-DRIVEN EVALUATION OF PROOF-OF-CONCEPT IN-EDGE AI FRAMEWORK A.  ... 
arXiv:1809.07857v2 fatcat:2sav5fnozbc3rd2atcgpxzg7jq

Multi-Access Edge Computing: A Survey

Abderrahime Filali, Amine Abouaomar, Soumaya Cherkaoui, Abdellatif Kobbane, Mohsen Guizani
2020 IEEE Access  
Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems.  ...  Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters.  ...  Moreover, they construct a distributed algorithm based on the computation offload-ing game model to maximize the utility of each vehicle.  ... 
doi:10.1109/access.2020.3034136 fatcat:ncqn7le65jfshcqnqzy7twrwwu

A Survey on Mobile Edge Computing: The Communication Perspective [article]

Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, Khaled B. Letaief
2017 arXiv   pre-print
Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing  ...  A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation  ...  One possible approach is to offload the computation-intensive data to the edge servers at BSs that have huge computation capabilities in order to reduce the server-computing time; while the components  ... 
arXiv:1701.01090v4 fatcat:txpdpkp4irhibf6btbzvgth4n4

Vehicular Edge Computing via Deep Reinforcement Learning [article]

Qi Qi, Zhanyu Ma
2020 arXiv   pre-print
Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for Vehicle of Internet, which provides the optimal policy directly from the environment  ...  edge computing node, the decision should consider numerous factors.The offloading decision work mostly formulate the decision to a resource scheduling problem with single or multiple objective function  ...  Finally, each time the vehicular edge computing environment changing, the offloading decision should be re-computed, which results in more service delay and higher cost.  ... 
arXiv:1901.04290v3 fatcat:2ubcmtfm7ne3djdoj6pwxm6aie

Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices [article]

Bin Lin, Yinhao Huang, Jianshan Zhang, Junqin Hu, Xing Chen, Jun Li
2019 arXiv   pre-print
A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints.  ...  This approach considers the characteristics of DNNs partitioning and layers offloading over the cloud, edge and end devices.  ...  Offloading DNN layers in hybrid computing environments is a discrete problem, and it needs a new coding approach.  ... 
arXiv:1907.13306v1 fatcat:rq43mcbs3ja5fnvl453hyizbru

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Habiba Mohamed, Eyhab Al-Masri, Olivera Kotevska, Alireza Souri
2022 Electronics  
Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures  ...  We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive  ...  The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or  ... 
doi:10.3390/electronics11182888 fatcat:v3bsedblgvfplgxd27isommb64

Parked Vehicles Task Offloading in Edge Computing

Khoa Nguyen, Steve Drew, Changcheng Huang, Jiayu Zhou
2022 IEEE Access  
Additionally, we take a cumulative incentives model into account, where the PV owners are able to earn profit by sharing their computation resources.  ...  INDEX TERMS Parked vehicles, cloud computing, edge computing, collaborative cloud-edge computing, online task offloading, container orchestration, Kubernetes.  ...  ACKNOWLEDGMENT The authors would like to thank their academic editor and anonymous reviewers for their careful reading of their manuscript and many insightful comments and suggestions.  ... 
doi:10.1109/access.2022.3167641 fatcat:af26bkqrzzahhewdoeklgcisbu

Edge Intelligence: Architectures, Challenges, and Applications [article]

Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
2020 arXiv   pre-print
In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence.  ...  We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed  ...  Training Acceleration Training a model, especially deep neural networks, is often too computationally intensive, which may result in low training efficiency on edge devices, due to their limited computing  ... 
arXiv:2003.12172v2 fatcat:xbrylsvb7bey5idirunacux6pe

PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services [article]

Jie Tang, Shaoshan Liu, Bo Yu, Weisong Shi
2018 arXiv   pre-print
To the best of our knowledge, this is the first complete edge computing system of a production autonomous vehicle.  ...  To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented π-Edge, a complete edge computing framework for autonomous robots and vehicles.  ...  by building a cost function for local computing versus offloading to edge cloud node i.  ... 
arXiv:1901.04978v1 fatcat:pq7awwlmvran3lu4s3pb7oo55a

Communication-Computation Trade-Off in Resource-Constrained Edge Inference [article]

Jiawei Shao, Jun Zhang
2020 arXiv   pre-print
It focuses on device-edge co-inference, assisted by an edge computing server, and investigates a critical trade-off among the computation cost of the on-device model and the communication cost of forwarding  ...  A three-step framework is proposed for the effective inference: (1) model split point selection to determine the on-device model, (2) communication-aware model compression to reduce the on-device computation  ...  This article aims to fill this gap and introduce new design problems and methodologies for edge inference by presenting a delicate trade-off between communication overhead and on-device computation cost  ... 
arXiv:2006.02166v2 fatcat:p34n6l5aefbd5p3yxyzpbosatm

Multimedia Edge Computing [article]

Zhi Wang, Wenwu Zhu, Lifeng Sun, Han Hu, Ge Ma, Ming Ma, Haitian Pang, Jiahui Ye, Hongshan Li
2021 arXiv   pre-print
In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals' geographical preference and mobility behaviors,  ...  We provide both a retrospective view of recent rapid migration (resp. merge) of cloud multimedia to (resp. and) edge-aware multimedia and insights on the fundamental guidelines for designing multimedia  ...  [40] studied computation offloading using the edge infrastructure, including decision on computation offloading, allocation of computing resource within the mobile edge computing and mobility management  ... 
arXiv:2105.02409v1 fatcat:26eyjzgg2na2lep2crb4cfhs7m

SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing [article]

Davide Callegaro and Marco Levorato and Francesco Restuccia
2021 arXiv   pre-print
Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading.  ...  To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework.  ...  To the best of our knowledge, SeReMAS is the first framework addressing the problem of redundant task offloading in MAS with a data-driven approach which efficacy is verified in a realworld testbed and  ... 
arXiv:2105.15105v2 fatcat:yol3jdjyr5g6bbciftvpblj7ca

Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing [article]

Wei Xu, Zhaohui Yang, Derrick Wing Kwan Ng, Marco Levorato, Yonina C. Eldar, M'erouane Debbah
2022 arXiv   pre-print
Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange.  ...  For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem.  ...  For modeling cooperative computation offloading, an MADRL-based method was proposed in [238] to minimize the overall network computation cost.  ... 
arXiv:2206.00422v1 fatcat:osp426emrngi3bvye6fmk7kqce

Special issue: Elastic computing from edge to the cloud environments

Shashikant Ilager, Vlado Stankovski, Shrideep Pallickarar, Rajkumar Buyya
2021 Software, Practice & Experience  
Special issue: Elastic computing from edge to the cloud environments 1 INTRODUCTION We are pleased to present a special issue that focuses on state-of-the-art research on Elastic Computing from Edge to  ...  multiple computing tiers, from the Edge to the Data Center/Cloud.  ...  More importantly, thanks to the reviewers for their valuable contributions in providing thoughtful comments and improving the quality of articles.  ... 
doi:10.1002/spe.3012 fatcat:ilg7ttblqjhwdkp5qo4gcfst7u
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