Energy- and Resource-Aware Computation Offloading for Complex Tasks in Edge Environment

Peng, Kai; Zhao, Bohai; Xue, Shengjun; Huang, Qilin

doi:10.1155/2020/9548262

Cited by 11 publications

(2 citation statements)

References 47 publications

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“…Based on Elman network, this study predicted the short-term stock price in the future and achieved a good prediction effect. However, it is unrealistic to predict the long-term stock price in the future, which is difficult to achieve [28][29][30].…”

Section: Discussionmentioning

confidence: 99%

An Improved Elman Network for Stock Price Prediction Service

Liu

Cao

2020

Security and Communication Networks

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The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

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Section: Discussionmentioning

confidence: 99%

An Improved Elman Network for Stock Price Prediction Service

Liu

Cao

2020

Security and Communication Networks

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“…As a consequence, IoT applications can retrieve these data without going through the backhaul link, resulting in a significant reduction in data transmission [6]. Furthermore, it also saves the energy consumption of edge servers by reducing the non-essential utilization of backhaul connections [7], and further decreases the energy cost of IoT devices by minimizing the time to obtain IoT data [8].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach

et al. 2020

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Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.INDEX TERMS Cooperative edge caching, Internet of Things, multi-agent deep learning, actor-critic, multi-agent deep deterministic policy gradient.

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