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22 pages, 1121 KiB

Open AccessArticle

Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

by Zhe Zheng, Yu Han, Yingying Chi, Fusheng Yuan, Wenpeng Cui, Hailong Zhu, Yi Zhang and Peiying Zhang

Electronics 2023, 12(15), 3330; https://doi.org/10.3390/electronics12153330 - 3 Aug 2023

Cited by 4 | Viewed by 2340

Abstract

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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13 pages, 2000 KiB

Open AccessArticle

Transfer Learning for Multi-Premise Entailment with Relationship Processing Module

by Pin Wu, Rukang Zhu and Zhidan Lei

Future Internet 2021, 13(3), 71; https://doi.org/10.3390/fi13030071 - 13 Mar 2021

Viewed by 2774

Abstract

Using the single premise entailment (SPE) model to accomplish the multi-premise entailment (MPE) task can alleviate the problem that the neural network cannot be effectively trained due to the lack of labeled multi-premise training data. Moreover, the abundant judgment methods for the relationship between sentence pairs can also be applied in this task. However, the single-premise pre-trained model does not have a structure for processing multi-premise relationships, and this structure is a crucial technique for solving MPE problems. This paper proposes adding a multi-premise relationship processing module based on not changing the structure of the pre-trained model to compensate for this deficiency. Moreover, we proposed a three-step training method combining this module, which ensures that the module focuses on dealing with the multi-premise relationship during matching, thus applying the single-premise model to multi-premise tasks. Besides, this paper also proposes a specific structure of the relationship processing module, i.e., we call it the attention-backtracking mechanism. Experiments show that this structure can fully consider the context of multi-premise, and the structure combined with the three-step training can achieve better accuracy on the MPE test set than other transfer methods. Full article

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