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Towards Computation Offloading in Edge Computing: A Survey

Congfeng Jiang, Xiaolan Cheng, Honghao Gao, Xin Zhou, Jian Wan
2019 IEEE Access  
Moreover, resource scheduling approaches, gaming and tradeoffing among system performance and overheads for computation offloading decision making are also reviewed.  ...  In this paper a thorough literature review is conducted to reveal the state-of-the-art of computation offloading in edge computing.  ...  Researchers proposed energy saving approaches for resource allocation of single user and multiuser mobile edge computing offloading systems (MECO).  ... 
doi:10.1109/access.2019.2938660 fatcat:qcpqojzxsnbsnmuez3x2ew4sqa

Resource Scheduling in Edge Computing: A Survey [article]

Quyuan Luo, Shihong Hu, Changle Li, Guanghui Li, Weisong Shi
2021 arXiv   pre-print
Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed.  ...  With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm  ...  For example, Meng et al. [230] proposed a game-theoretic based resource allocation mechanism to optimally allocate resources for each component task of a mobile application.  ... 
arXiv:2108.08059v1 fatcat:oo4lepcn3rhdfafefw5lkq2lia

Q-learning Based Task Offloading and Resources Optimization for a Collaborative Computing System

Zihan Gao, Wanming Hao, Zhuo Han, Shouyi Yang
2020 IEEE Access  
and Resources Optimization for Collaborative computing SystemFIGURE 2.  ...  As for the first three problems, we still implemented the hierarchical computing scheme in the collaborative cloud computing system to optimize resource allocation.  ... 
doi:10.1109/access.2020.3015993 fatcat:h2jdam2wjndefi3hpgenzocz5e

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Hongjing Ji, Osama Alfarraj, Amr Tolba
2020 IEEE Access  
Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay.  ...  Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory  ...  be optimally allocated to each task to realize VOLUME 8, 2020 the objectives of minimizing the delay and preserving the user's device battery life. 2) COLLABORATIVE MOBILE EDGE CLOUD COMPUTING Collaborative  ... 
doi:10.1109/access.2020.2983609 fatcat:b45abdrxbracnbfpvtvtu5uxui

Load Optimization Based On Edge Collaboration in Software Defined Ultra-Dense Networks

Peng Yang, Yifu Zhang, Ji Lv
2020 IEEE Access  
However, the heterogeneity of servers, the distributed resources, and the dynamic energy consumption of mobile devices in ultra-dense networks make it extremely difficult for users to offload and load  ...  Edge computing can effectively guarantee the low-latency requirements of users in ultra-dense networks.  ...  In [27] , the strategy of using deep reinforcement learning to allocate computing resources in mobile edge computing networks was studied.  ... 
doi:10.1109/access.2020.2973041 fatcat:3aq3fs6vsrcjvbcazrkgyxlshe

IEEE Access Special Section Editorial: Communication and Fog/Edge Computing Toward Intelligent Connected Vehicles (ICVS)

Lei Shu, Junhui Zhao, Yi Gong, Changqing Luo, Tim Gordon
2021 IEEE Access  
These kinds of applications typically require significant computing power to perform computation-intensive and latency-sensitive tasks generated by the vehicle sensors for low-latency response.  ...  To tackle the challenge, fog/edge computing is proposed as innovative computing paradigms to extend computing capacity to the network edge to meet the requirements.  ...  In the article ''Joint computation offloading and URLLC resource allocation for collaborative MEC-assisted cellular-V2X networks,'' by Feng et al., a joint computation and URLLC resource allocation strategy  ... 
doi:10.1109/access.2021.3071260 doaj:3c15b73f232c41a199e81c178ee3a34d fatcat:c5xvpfeh3zf5fgyzkpzmdkw3jy

QoE-traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming

Abbas Mehrabi, Matti Siekkinen, Antti Yla-Jaaski
2018 IEEE Access  
We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the  ...  Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.  ...  His main research interests include quality of experience optimization and resource allocation for multimedia services in mobile edge computing environments, energy efficient mobile computing, and scheduling  ... 
doi:10.1109/access.2018.2870855 fatcat:szm3bgxe7vfzpefkksacf3mn6e

Joint Computation Offloading and URLLC Resource Allocation for Collaborative MEC assisted Cellular-V2X Networks

Lei Feng, Wenjing Li, Yingxin Lin, Liang Zhu, Shaoyong Guo, Zerui Zhen
2020 IEEE Access  
INDEX TERMS Cellular V2X networks, URLLC radio resource management, collaborative mobile edge computing, power optimization, latency and reliability.  ...  Considering the importance of both reliability and delay in vehicle communication, this article innovatively envisions a joint computation and URLLC resource allocation strategy for collaborative MEC assisted  ...  To satisfy users' experience in vehicular mobile edge computing, an adaptive computational resource allocation method is investigated in [5] .  ... 
doi:10.1109/access.2020.2970750 fatcat:ywuhez3zuva5fbhlbl43wfhrcu

Analytical Study of Task Offloading Techniques using Deep Learning

Mr Almelu, Dr. S. Veenadhari, Kamini Maheshwar
2021 SMART MOVES JOURNAL IJOSCIENCE  
The Internet of Things (IoT) systems create a large amount of sensing information. The consistency of this information is an essential problem for ensuring the quality of IoT services.  ...  As a result, the issue of incomplete information must be addressed as soon as feasible by offloading duties such as predictions or assessment to the network's edge devices.  ...  Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks AI based task allocation algorithm (iRAF) has been proposed for the collaborative mobile edge computing network  ... 
doi:10.24113/ijoscience.v7i7.393 fatcat:4ijltsc42fei5lh6cnqjc4iokq

Special Issue on Artificial-Intelligence-Powered Edge Computing for Internet of Things

Lei Yang, Xu Chen, Samir M. Perlaza, Junshan Zhang
2020 IEEE Internet of Things Journal  
task latency for all the IoT users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing system. 2327-4662 c 2020 IEEE.  ...  In the article "Joint DNN partition deployment and resource allocation for delay-sensitive deep learning inference in IoT," He et al. studied joint optimization of partition deployment and resource allocation  ... 
doi:10.1109/jiot.2020.3019948 fatcat:mogalqnhnnaqpbxb7zivzdhvry

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dimitrios Dechouniotis, Nikolaos Athanasopoulos, Aris Leivadeas, Nathalie Mitton, Raphaël M. Jungers, Symeon Papavassiliou
2020 Sensors  
It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together  ...  Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical  ...  Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/s20082191 pmid:32294937 pmcid:PMC7218846 fatcat:j3hbgmgaarcpjegnnaqq4oq6ei

Edge service resource allocation strategy based on intelligent prediction [article]

Yujie Wamg, Xin Du, Xuzhao Chen, Zhihui Lu
2021 arXiv   pre-print
Artificial intelligence is one of the important technologies for industrial applications, but it requires a lot of computing resources and sensor data to support it.  ...  With the development of edge computing and the Internet of Things, artificial intelligence are playing an increasingly important role in the field of edge services.  ...  The strategy through collaborative mobile devices and edge server, make full use of the convenience of smart mobile terminals and the edge server powerful computing ability, considering the complexity  ... 
arXiv:2107.12740v1 fatcat:oxac3lmmongvxdctf3hohr7tee

An Edge-Computing Based Architecture for Mobile Augmented Reality [article]

Jinke Ren, Yinghui He, Guan Huang, Guanding Yu, Yunlong Cai, and Zhaoyang Zhang
2018 arXiv   pre-print
In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising  ...  means to deliver better quality of experience (QoE) for AR consumers.  ...  By distributing the conventional centralized cloud computing resources to the edge of mobile networks, MEC offers an adjacent computing environment for mobile subscribers and provides a variety of benefits  ... 
arXiv:1810.02509v2 fatcat:nig7txlx6ncq3fkkywkggukxgi

D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming

Abbas Mehrabi, Matti Siekkinen, Gazi Illahi, Antti Yla-Jaaski
2019 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)  
Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network.  ...  Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.  ...  ACKNOWLEDGMENT This research has been financially supported by Lacrimosa project grant number 297892 and the Nokia Center for Advanced Research.  ... 
doi:10.1109/wowmom.2019.8792981 dblp:conf/wowmom/MehrabiSIY19 fatcat:2st3vkjxfrd2tckh3ogpq2efoe

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Yunzhao Li, Feng Qi, Zhili Wang, Xiuming Yu, Sujie Shao
2020 IEEE Access  
In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing.  ...  Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize  ...  The work in [29] proposed an optimization framework for computation offloading and resource allocation for multi-server mobile edge computing system.  ... 
doi:10.1109/access.2020.2991773 fatcat:6nb6dfjjjbaudpxvdq4v7jpdry
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