On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks

Wang, Dongyu; Lan, Yanwen; Zhao, Tiezhu; Yin, Zhenping; Wang, Xiaoxiang

doi:10.1109/access.2018.2876893

Cited by 25 publications

(13 citation statements)

References 45 publications

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“…Based on recent advances in antenna design, the study [146] has proposed an energy efficient offloading scheme using full duplex (FD) relays. The network consists of one BS and several UEs forming multiple clusters.…”

Section: Energy Consumption Minimizationmentioning

confidence: 99%

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Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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Section: Energy Consumption Minimizationmentioning

confidence: 99%

“…The study [146] has proposed a caching scheme that takes advantage of D2D multicast. The system model consists of one BS and several UEs forming multiple clusters according to their geographical locations.…”

Section: Energy Consumption Minimizationmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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“…In [11], a D2D-assisted caching strategy is investigate, and a non-parametric estimator is proposed to estimate a optimal user file pairs for efficient caching. Moreover, In [12], Wang et al . study the computation and traffic offloading in cache-aided device-to-device multicast networks for the content delivery and delay sensitive task offloading services.…”

Section: Introductionmentioning

confidence: 99%

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Tang

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload problems in internet of things (IoT). Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviours. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q-network (DQN) algorithm are respectively applied to user devices and SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables user devices and SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

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“…As known to all, duplicate contents transportation causes most edge-to-edge data traffic. Some researchers tried to reduce the pressure on backbone by using IP Multicast technology [8], [9]. As an online solution to traffic reducing problem, IP Multicast allows application to adopt functionality at protocol level without significantly impacting the edge network.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Caching Scheme Using Non-Cooperative Game for Mobile Edge Networks

Wang

2020

IEEE Access

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Mobile edge storage is an emerging computing paradigm which enables users to enjoy high quality of experience when they access cloud services. However, extremely fast increasing multimedia data traffic from cloud to mobile users comes with the challenges of low-latency and high-bandwidth over cloud computing model. How to design cache strategies by exploiting terminal nodes' utility is still a challenging issue. In this paper, by exploiting 5G technology and cooperative edge computing, we propose a cooperative edge caching framework to tackle three issues simultaneously in the application scenarios of massive short video services: (1) reducing data transportation latency to improve mobile uses' quality of experience; (2) mitigating data traffic pressure on backbone network; (3) decreasing the workload on cloud servers. Firstly, we define a cooperative edge caching network model, in which many edge nodes equipped with capacities of storage, user admittance and Internet access form an ad hoc network and each node serves multiple mobile users. Secondly, we design a non-cooperative game model to investigate the cooperation behavior among the edge nodes, we allow each edge node server as a player and make decision of cache (storage) initialization and replacement strategies. Thirdly, we find there is a Nash Equilibrium in the proposed game. Finally, we design a distributed algorithm to achieve the equilibrium according to our theoretical analysis. Experiment results show that the proposed game-based edge caching strategy can improve mobile uses' quality of experience by 20% latency decreasing and reduce 80% backbone traffic and cloud workload. INDEX TERMS Non-cooperative game theory, Nash equilibrium, distributed cache placement, cache hit rate, average edge utility

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On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks

Cited by 25 publications

References 45 publications

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

A Distributed Caching Scheme Using Non-Cooperative Game for Mobile Edge Networks

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