With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discover...
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With the development of service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of...
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Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in cla...
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In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve suc...
In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization.
Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provi...
Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provides service closer to the end-users. By processing tasks with abundant idle resources at the network edge, low-latency demands for some tasks can be met. However, the mobility and uncertainty of vehicles pose significant challenges to vehicle computation offloading. This paper focuses on the decision-making process of vehicle computation offloading, specifically task partitioning and scheduling decisions. This paper summarizes some hot problems and solutions, including latency optimization, reliability optimization, energy efficiency optimization, cost optimization, and mobility support. This study will help researchers discover important features of vehicle computation offloading and find the most suitable scheme to solve the vehicle offloading problem in different scenarios.
In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introdu...
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In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification imple- mentation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN-based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.
With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting th...
With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model; second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed; finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes.
Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical fe...
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With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires su...
With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user’s cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms.
Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitorin...
Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.
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