2018 IEEE International Conference on Networking, Architecture and Storage (NAS)最新文献_第3页

NAS 2018 Keynotes

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/nas.2018.8515690

引用次数: 0

Automatic Identification Technology of Rotor UAVs Based on 5G Network Architecture 基于5G网络架构的旋翼无人机自动识别技术

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515719

Tao Yang, Jingcheng Zhao, T. Hong, Weishi Chen, Xinru Fu

{"title":"Automatic Identification Technology of Rotor UAVs Based on 5G Network Architecture","authors":"Tao Yang, Jingcheng Zhao, T. Hong, Weishi Chen, Xinru Fu","doi":"10.1109/NAS.2018.8515719","DOIUrl":"https://doi.org/10.1109/NAS.2018.8515719","url":null,"abstract":"UAVs (Unmanned Aerial Vehicles), also called drones, have drawn the attention of researchers owing to its flexibility, threatening and enormous application value. The construction of 5G network brings a new direction of detecting, identifying, and managing UAVs based on the native cloud architecture. In 5G end-to-end network slices, rotor UAVs are detected and identified by deploying 5G millimeter waves and using a joint algorithm, the improved short-time Fourier transform (STFT) and based on Bessel function base. For one-rotor UAV, the use of STFT following conjugation of sinusoidal frequency modulation (SFM) radar echo data based on millimeter wave doubles the recognition effect compared with the unconjugated processing. For multi-rotors UAV, the number of rotors and the length and rotational speed of each rotor are effectively identified through projection on the SFM data and the introduction of k order Bessel function. According to the results of automatic identification of UAVs by 5G native cloud architecture, the high bandwidth and low delay of 5G network provide a reliable basis for the resolution. Because of good robustness of the Bessel function, it provides an effective solution for the detection, identification and management of UAVs by 5G millimeter wave radar.","PeriodicalId":115970,"journal":{"name":"2018 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124099637","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: 6

A Worm Containment Approach Towards Online Social Networks 针对在线社交网络的蠕虫遏制方法

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515718

Zhaolong Zhang, Zhichao Zhu

引用次数: 0

Personalized Behavior Prediction with Encoder-to-Decoder Structure 基于编码器到解码器结构的个性化行为预测

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515696

Tong Yin, Xiaotie Deng, Yuan Qi, Wei Chu, Jing Pan, X. Yan, Junwu Xiong

{"title":"Personalized Behavior Prediction with Encoder-to-Decoder Structure","authors":"Tong Yin, Xiaotie Deng, Yuan Qi, Wei Chu, Jing Pan, X. Yan, Junwu Xiong","doi":"10.1109/NAS.2018.8515696","DOIUrl":"https://doi.org/10.1109/NAS.2018.8515696","url":null,"abstract":"With the rise of the Internet industry and the technique of artificial intelligence, personalized services are increasingly important in recent years for improving user experience and increasing corporates' competitiveness and profits. Precise prediction of customers' behaviors has shown great effects in modern business marketing, especially when making personalized decisions. In this paper, we develop a deep learning network to make personalized predictions of their behaviors among a list of potential choices. The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also modify the long-short- term memory (LSTM) cell forget gate's structure to enhance the attention ability. Such design, called the attetioned LSTM, converges quicker and better while still maintain the similar performance in open dataset IMDB. In addition, in dealing with personalized prediction problems in real-world datasets provided by our cooperative company, our attentioned LSTM achieves a 10% higher precision in average than the standard LSTM model. The advantage is confirmed in evaluation of this generic method on a real dataset of users' behaviors sequences and individuals' attribute profiles from Ant Financial. It also achieves a great result working on the real-world business scene. This model can also achieve a great performance working on the real-world business scene.","PeriodicalId":115970,"journal":{"name":"2018 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127587539","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

Energy-Efficient Task Offloading and Resource Scheduling for Mobile Edge Computing 移动边缘计算节能任务卸载与资源调度

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515731

Hongyan Yu, Quyuan Wang, Songtao Guo

引用次数: 39

Accelerating Spark Shuffle with RDMA 加速Spark Shuffle与RDMA

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515724

Bing Liu, Fang Liu, Nong Xiao, Zhiguang Chen

{"title":"Accelerating Spark Shuffle with RDMA","authors":"Bing Liu, Fang Liu, Nong Xiao, Zhiguang Chen","doi":"10.1109/NAS.2018.8515724","DOIUrl":"https://doi.org/10.1109/NAS.2018.8515724","url":null,"abstract":"Apache Spark is a lightning-fast unified analytics engine for large-scale data processing. When executing an application with Spark, it runs many jobs in parallel. These jobs are divided into stages based on the shuffle boundary. However, shuffling data across the stages in a cluster is time-consuming because it will place significant burden on operating system on both the source and the destination by requiring many remote files and network I/Os. Meanwhile, the latest Spark is based on Netty which is written with Java Sockets and will produce a large number of data copies during the shuffle phase. This has become the major bottleneck for Apache Spark and motivates us to use RDMA technology to accelerate data shuffle. RDMA, with the function of zero-copy transfers, reducing latency and CPU overhead, can reduce stress on operating system during the shuffle phase and improve the throughput of the whole system. In this paper, we present a high-performance RDMA-based design for accelerating data shuffle in Apache Spark framework by providing tiering memory pool and different mechanisms to transfer messages of different sizes. The experimental results show that compared to the default Spark running with IP over InfiniBand (IPoIB), our proposed design can achieve up to 89.8% performance improvement for Spark RDD operation benchmarks (e.g., GroupBy and SortBy), up to 49% performance improvement for iterative algorithms (e.g., TriangleCount and SVM in SparkBench). And the evaluation results also show that our RDMA-based design slightly outperforms Crail-Spark-IO, a recent open-source Spark shuffle plugin from IBM.","PeriodicalId":115970,"journal":{"name":"2018 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126348500","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

Exporting Transactional Interface to Applications in Log-Structured File Systems 导出事务接口到日志结构文件系统中的应用程序

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515726

Jiacheng Zhang, Youyou Lu, Keni Qiu, Zejun Shi, Hongsuk Choi, J. Shu

引用次数: 0

Joint Optimization of Energy and QoE with Fairness in Cooperative Fog Computing System 协同雾计算系统中具有公平性的能量和QoE联合优化

2018 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2018-10-01 DOI: 10.1109/NAS.2018.8515738

Yifan Dong, Cheng Han, Songtao Guo

引用次数: 13