2019 IEEE International Conference on Power Data Science (ICPDS)最新文献_第2页

Record Completeness Evaluation Based on Multiple Data Sources 基于多数据源的记录完整性评价

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017199

Aman Wu, Lingli Li, Ping Xuan

引用次数: 0

The Research on Flow Table Optimization Based on Dynamic Timeout Mechanism 基于动态超时机制的流表优化研究

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017185

Zhaohui Ma, Gansen Zhao, Chengchuang Lin, Haoyu Luo, Yuanfu Zhong, Shuangyin Li, Qinglan Wu, Zefeng Mo, Zanbo Zhang

{"title":"The Research on Flow Table Optimization Based on Dynamic Timeout Mechanism","authors":"Zhaohui Ma, Gansen Zhao, Chengchuang Lin, Haoyu Luo, Yuanfu Zhong, Shuangyin Li, Qinglan Wu, Zefeng Mo, Zanbo Zhang","doi":"10.1109/ICPDS47662.2019.9017185","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017185","url":null,"abstract":"SDN(Software Defined Networking) is a new network architecture, which decouples the control plane from data plane and operates the globle network with elaborate abstraction. The control plane advocates a centralized approach of network control, which provides both flexibility and high efficiency. However, there are lots of problems in this architecture and the bottleneck problem is the network's performance. Existing OpenFlow based flow expiry mechanisms relys on a fixed timeout after which the switch proactively removes the flow entries from its flow table which may causes unnecessary flow table occupation. Therefore, in this paper, a strategy of dynamically adjusting timeout time is proposed and the program is implemented. It can dynamically adjust the timeout time according to the actual situation of equipment and data flow, and can better adapt to the network with different data flows. The experiment shows that the strategy can improve the network's performance and the mechanism designed in this paper is proved to be effective.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127438194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Effective Source Selection Algorithm for Filling Missing Tuples 一种填充缺失元组的有效源选择算法

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017179

Hengzhen Xie, Lingli Li, Ping Xuan

引用次数: 1

Research on a Power Meter Reading Method Based on Wide and Narrow Conversion 基于宽窄转换的电能表抄表方法研究

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017173

Cui Wanhe, Hu Rizha, Zhao Xin

引用次数: 0

Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression 基于线性回归的个性化协同过滤推荐算法

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017166

Jia Wu, Chao Liu, Wei Cui, Yuxiao Zhang

引用次数: 3

Dynamic Analysis and Control Measures of Distribution Network Voltage with Electric Arc Furnace 电弧炉配电网电压动态分析及控制措施

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017177

Yanfeng Xia, Zhenyu Shi, Yanan Li, Yunpeng Feng, Zhilin Xu

引用次数: 1

Research on Energy Data Acquisition and Interaction Algorithm Based on Wide-Narrow Conversion 基于宽窄转换的能源数据采集与交互算法研究

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017183

Liu Ran, Yun Peng, Mi Jia

引用次数: 0

Optimization Strategy of Flow Table Storage Based on “Betweenness Centrality” 基于“中间性中心性”的流表存储优化策略

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017175

Zhaohui Ma, Yan Yang

{"title":"Optimization Strategy of Flow Table Storage Based on “Betweenness Centrality”","authors":"Zhaohui Ma, Yan Yang","doi":"10.1109/ICPDS47662.2019.9017175","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017175","url":null,"abstract":"With the gradual progress of cloud computing, big data, network virtualization and other network technology. The traditional network architecture can no longer support this huge business. At this time, the clean slate team defined a new network architecture, SDN (Software Defined Network). It has brought about tremendous changes in the development of today's networks. The controller sends the flow table down to the switch, and the data flow is forwarded through matching flow table items. However, the current flow table resources of the SDN switch are very limited. Therefore, this paper studies the technology of the latest SDN Flow table optimization at home and abroad, proposes an efficient optimization scheme of Flow table item on the betweenness centrality through the main road selection algorithm, and realizes related applications by setting up experimental topology. Experiments show that this scheme can greatly reduce the number of flow table items of switches, especially the more hosts there are in the topology, the more obvious the experimental effect is. And the experiment proves that the optimization success rate is over 80%.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127305427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reactive Power Optimization based on Data-driven Load Curve Segmentation 基于数据驱动的负荷曲线分割的无功优化

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017190

Yaqiong Li, Tongxun Wang, Zhanfeng Deng

引用次数: 1

Mining of Attribute Network Community based on Structure and Node Similarity 基于结构和节点相似度的属性网络社区挖掘

2019 IEEE International Conference on Power Data Science (ICPDS) Pub Date : 2019-11-01 DOI: 10.1109/ICPDS47662.2019.9017174

Xiaowei Zhuang, Yan Yang, Yuhang Li

{"title":"Mining of Attribute Network Community based on Structure and Node Similarity","authors":"Xiaowei Zhuang, Yan Yang, Yuhang Li","doi":"10.1109/ICPDS47662.2019.9017174","DOIUrl":"https://doi.org/10.1109/ICPDS47662.2019.9017174","url":null,"abstract":"Mining cohesive subgraphs from a network is an important direction in network analysis. Most of the existing methods are based on the topology of common networks, which ignores the rich information of the nodes in the real network. The k-truss model which is proposed by user engagement and tie strength, captures the degree of strong connection among users who participate in the network with other users actively. However, this model does not consider the attributes of users. In order to find the cohesive subgraphs on social networks efficiently and accurately, this paper proposes a new model (k,r)-truss on the attribute network community based on k-truss, and finds the cohesive subgraphs on the social network from the perspective of strong connection and similarity between users. The problem of enumerating all maximal (k,r)-truss is NP-hard, so in order to speed up the calculation, this paper proposes new pruning algorithms AdvEnumH and AdvEnumHC, which reduces the search space of the mining process significantly. Finally, the experiments are carried out on the real data set to evaluate the performance of the proposed algorithm. The results of experiments demonstrate that our algorithm has significantly improved in efficiency and timeliness compared with the current best method.","PeriodicalId":130202,"journal":{"name":"2019 IEEE International Conference on Power Data Science (ICPDS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132534390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0