2017 International Conference on Networking, Architecture, and Storage (NAS)最新文献

SpyStorage: A Highly Reliable Multi-Cloud Storage with Secure and Anonymous Data Sharing SpyStorage:一个高度可靠的多云存储与安全和匿名数据共享

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026878

Pengyan Shen, Wan Liu, Zheng Wu, Mingzhong Xiao, Quanqing Xu

{"title":"SpyStorage: A Highly Reliable Multi-Cloud Storage with Secure and Anonymous Data Sharing","authors":"Pengyan Shen, Wan Liu, Zheng Wu, Mingzhong Xiao, Quanqing Xu","doi":"10.1109/NAS.2017.8026878","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026878","url":null,"abstract":"Users outsourcing their critical data to cloud services has been increasingly popular recently. System reliability and data security have become the top priority to be concerned because of the possible service failures, unauthorized data access and privacy leaks in the cloud storage system. However, traditional single cloud storage service is far from meeting the demands because of the inevitable problems of data loss, vendor-lock in and privacy leaks during data storage and share. In this paper, a novel design is to be proposed and named by SpyStorage that offers users a highly reliable storage service and a secure anonymous data sharing mechanism based on Multi-Cloud by implementing the quorum protocol and utilizing the ABE/ABS cryptographic model. To the best of our knowledge, this is the first work using ABE and ABS to completely solve the issues in secret storing and sharing with anonymity simultaneously in the Multi- Cloud field. Extensive measuring experiments have proved that SpyStorage can achieve a relative high reliability with a low tradeoff of file transfer speed.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132047012","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: 5

Detecting Information Theft Based on Mobile Network Flows for Android Users 基于移动网络流量的Android用户信息窃取检测

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026853

Zhenyu Cheng, Xunxun Chen, Yongzheng Zhang, Shuhao Li, Yafei Sang

{"title":"Detecting Information Theft Based on Mobile Network Flows for Android Users","authors":"Zhenyu Cheng, Xunxun Chen, Yongzheng Zhang, Shuhao Li, Yafei Sang","doi":"10.1109/NAS.2017.8026853","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026853","url":null,"abstract":"With the widespread use of smartphones, more and more malicious attacks happen with information leakage from apps installed on users' devices. The adversary always uses a malware as the client to take remote control of smartphones, and leverages the vulnerability of operation systems to send back the collected information without users' permissions. All the information has to be transferred by network traffic. In this paper, we consider that different apps maybe generate different network flows by different operations, and the ``shapes\" of the benign flows and malicious ones will be diverse. Thus we propose a detection model based on the analysis of relationships between behavior patterns and network flows, which achieves our goal by using the Random Forest machine learning algorithm to classify the network flows into benign or malicious. To further improve the controllability of the experiment, we design an app called Moledroid to simulate malwares by uploading the user's privacy without authorization, in addition, we can change the behavior pattern of the app to complete our evaluation. Finally, we run this app and several benign apps to generate traffic to detect the malicious network flows, and it shows that our detection model can achieve precision and accuracy higher than 95%, which demonstrates that our model is suitable for detecting the network flows of information theft.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905009","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: 13

MTM: A Reliable Multiple Trees Multicast for Data Center Network MTM:用于数据中心网络的可靠多树组播

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026866

Xin Xiong, Tan Chen

引用次数: 3

Branch Prediction Migration for Multi-Core Architectures 多核架构的分支预测迁移

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026848

Tan Zhang, Chaobing Zhou, Libo Huang, Nong Xiao

引用次数: 1

A Hash-Based Space-Efficient Page-Level FTL for Large-Capacity SSDs 基于哈希的大容量ssd空间高效页级FTL

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026838

Fan Ni, Chunyi Liu, Yang Wang, Chengzhong Xu, Xiao Zhang, Song Jiang

{"title":"A Hash-Based Space-Efficient Page-Level FTL for Large-Capacity SSDs","authors":"Fan Ni, Chunyi Liu, Yang Wang, Chengzhong Xu, Xiao Zhang, Song Jiang","doi":"10.1109/NAS.2017.8026838","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026838","url":null,"abstract":"With increasing demands on high-performance and large-capacity SSDs in the enterprise-scale storage, the concern about the inefficient use of the DRAM space in SSDs rises, especially for those using page-level FTL (Flash Translation Layer). In such an FTL, the address mapping scheme allows a logical page address (LPA) to be mapped to any physical page address (PPA) in the disk. Though it provides flexible address management and minimizes internal data movements, it requires a large address mapping table whose size is proportional to the capacity of the disk. With the increase of SSD's capacity, the table can be too large to be held entirely in the DRAM buffer of the SSD, causing constantly accessing to the flash for the address translation. This performance penalty due to the buffer misses is particularly high with workloads of weak access locality and large working sets. In this paper, we propose a space- efficient page- level FTL using hash functions in the address translation, named Hash-based Page- level FTL, or HP-FTL in short, to address the concern. HP-FTL trades mapping flexibility with limited performance impact for high space efficiency allowing the entire table to fit in the buffer and eliminating translation misses. The experiment results show that HP-FTL can provide up to 2.6X throughput compared to DFTL, a representative page-level FTL, using the same amount of DRAM for buffering the table. Meanwhile, HP-FTL reduces the mapping table size to about 25% of the table space required by page- level mapping schemes, including DFTL, without having any buffer misses.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123074428","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: 5

Extending Lifetime of SSD in Raid5 Systems through a Reliable Hierarchical Cache 通过可靠的分层缓存延长Raid5系统中SSD的生存期

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026858

Rui Ye, Wentao Meng, Shenggang Wan

引用次数: 5

An Experimental Study on Deep Learning Based on Different Hardware Configurations 基于不同硬件配置的深度学习实验研究

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026843

Jingjun Li, Chen Zhang, Q. Cao, Chuanyi Qi, Jianzhong Huang, C. Xie

{"title":"An Experimental Study on Deep Learning Based on Different Hardware Configurations","authors":"Jingjun Li, Chen Zhang, Q. Cao, Chuanyi Qi, Jianzhong Huang, C. Xie","doi":"10.1109/NAS.2017.8026843","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026843","url":null,"abstract":"Deep learning has exhibited high accuracy and applicability in machine learning field recently, by consuming tremendous computational resources processing massive data. To improve the performance of deep learning, GPUs have been introduced to accelerate the training phase. The complex data processing infrastructure demands high-efficient collaboration among underlying hardware components, such as CPU, GPU, memory, and storage devices. Unfortunately, few work has presented a systematic analysis about the impact of hardware configurations on the overall performance of deep learning. In this paper, we aim to make an experimental study on a standalone system to evaluate how various hardware configurations affect the overall performance of deep learning. We conducted a series of experiments using varied configurations on storage devices, main memory, CPU, and GPU to observe the overall performance quantitatively. Based on analyzing these results, we found that the performance greatly relies on the hardware configurations. Specifically, the computation is still the primary bottleneck as double GPUs and triple GPUs shorten the execution time by 44% and 59% respectively. Besides, both CPU frequency and storage subsystem can significantly affect running time while the memory size has no obvious effect on the running time for training neural network models. We believe our experimental results can help shed light on further optimizing the performance of deep learning in computer systems.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125788186","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

WPS: A Workload-Aware Placement Scheme for Erasure-Coded In-Memory Stores WPS:一种工作负载感知的擦除编码内存存储放置方案

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026881

Shuang Wang, Jianzhong Huang, X. Qin, Q. Cao, C. Xie

{"title":"WPS: A Workload-Aware Placement Scheme for Erasure-Coded In-Memory Stores","authors":"Shuang Wang, Jianzhong Huang, X. Qin, Q. Cao, C. Xie","doi":"10.1109/NAS.2017.8026881","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026881","url":null,"abstract":"Data-intensive applications are increasingly depending on in-memory stores to meet high-I/O- performance requirements. To be resilient to server failures and in turn achieve high availability, both replication and erasure codes are introduced to in- memory stores. Since erasure codes have an advantage of memory efficiency over replication, we focus our work on erasure-coded in-memory stores and investigate placement schemes to address the issue of workload fluctuation. To mitigate the I/O imbalanced incurred by workload skew and maximize the utilization of all nodes, we proposed a ul{W}orkload-aware ul{P}lacement ul{S}cheme called WPS for Reed-Solomon-coded in- memory stores. WPS accomplishes balanced I/Os as follows: it divides in-memory data blocks into multiple groups based on access characteristics (e.g., popularity), and classifies all nodes into several groups according to nodes' access performance (e.g., indicated by available bandwidth), and places or migrates high-access- popularity in-memory data blocks to high-performance nodes without violating the essential principle of fault tolerance. The comparative experiments indicate that WPS can significantly improve load balancing for RS-coded in-memory stores exhibiting workload popularity skew; meanwhile, WPS achieves comparable mean, median, and tail latencies relative to two candidate placement schemes.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808230","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: 5

Development of Network Simulator for LWA/LAA Implementations 实现LWA/LAA的网络模拟器的开发

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026854

Kun-Hee Lee, Min-Taek Choi, Sungchul Choi, Jae-Hoon Kim

引用次数: 0

Balancing the Storage in a Deduplication Cluster 重复数据删除集群存储均衡

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026846

Giacomo Grangia, Quanqing Xu, A. Bianco, P. Giaccone

引用次数: 2