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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lin, Qiaoying Huang, Fangwan Yu, Zhiyong Li, Li College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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Electric Load Forecasting Based on Sparse Representation Model 13th

Electric Load Forecasting Based on Sparse Representation Mod...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Huang, Fangwan Zheng, Xiangping Yu, Zhiyong Yang, Guanyi Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030150921

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural network, SVM, and Random Forest. © 2019, Springer Nature Switzerland AG.

关键词： Electric load forecasting

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Research on Ontology-based network Security Knowledge Map

Research on Ontology-based Network Security Knowledge Map

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International Conference on Cloud computing, Big Data and Blockchain (ICCBB)

作者： Biqiong Chen Yanhua Liu YaNing Zheng Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781728112787

With the rapid development of network technology, security data is becoming more and more complex. Since the multi-source heterogeneous, complex scattered data security have been expanding, traditional relational database difficult to efficiently handle complex and diverse analysis of the relationship between big data security. In view of the above problems, this paper proposes an ontology-based network security knowledge map design method to more accurately describe the complex relationship between security elements. According to the actual needs of security analysis, using the ontological idea, design a multi-level security ontology system and study the relationship between ontology; Propose a mapping mechanism between the security ontology and the security knowledge map; The security knowledge map for multi-source data is constructed by using the graph database storage mode to provide important technical support for the construction and application research of the network security knowledge base.

关键词： Security Ontologies Communication networks Servers Correlation Data models

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Energy-Efficient Virtual Machine Migration Algorithm in Data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd International conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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X-Architecture Steiner Minimal Tree Construction Based on Discrete Differential Evolution 15th

X-Architecture Steiner Minimal Tree Construction Based on Di...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Wu, Hailin Xu, Saijuan Zhuang, Zhen Liu, Genggeng College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Department of Information Engineering Fujian Business University Fuzhou350012 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030324551

As the best connection model for the multi-pin net of the non-Manhattan architecture global routing problem, the X-architecture Steiner Minimum Tree (XSMT) construction is a Non-deterministic Polynomial hard (NP-hard) problem. The Differential Evolution (DE) algorithm has shown good application effect in solving various NP-hard problems. For this reason, based on the idea of DE algorithm, this paper proposes an XSMT construction algorithm for solving this problem. First of all, because the traditional DE algorithm is designed for continuous problems, the optimization ability is limited in solving discrete problems. This paper proposes a novel crossover operator and mutation operator. At the same time, in order to maintain the effectiveness of the evolutionary algorithm, an Edge-to-Point coding strategy suitable for evolutionary algorithms is proposed to better preserve the optimal substructure of the population. Finally, in order to speed up the convergence speed and quality of the algorithm, this paper proposes an initial solution based on the minimum tree generation algorithm. Experiments show that the effectiveness of the proposed algorithm and related strategies can construct a high-quality XSMT solution. © Springer Nature Switzerland AG, 2020.

关键词： Evolutionary algorithms

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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Accelerating Unsupervised Federated Graph Neural networks via Semi-asynchronous Communication 18th

Accelerating Unsupervised Federated Graph Neural Networks vi...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Liao, Yuanming Wu, Duanji Lin, Pengyu Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Graph neural networks have shown excellent performance in many fields owing to their powerful processing ability of graph data. In recent years, federated graph neural network has become a reasonable solution due to the enactment of privacy-related regulations. However, frequent communication between the coordinator and participants in federated graph neural network results in longer model training time and consumes many communication resources. To address this challenge, in this paper, we propose a novel semi-asynchronous federated graph learning communication protocol that simultaneously alleviates the negative impact of stragglers(slow participants) and accelerate the training process in the unsupervised federated graph neural network scenario. First, the weighted enforced synchronization strategy is intended to preserve the information carried by stragglers while preventing their stale models from harming the global model update. Second, the adaptive local update strategy is developed to make the local model of the participant with poor computing performance as close as possible to the global model. Experiments combine federated learning with graph contrastive learning. The results demonstrate that our proposed protocol outperforms the existing protocols in real-world networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet protocols

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