2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)最新文献

On Black-Box Monitoring Techniques for Multi-Component Services 多组件服务的黑盒监控技术研究

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548336

R. Filipe, Jaime Correia, Filipe Araújo, Jorge S. Cardoso

{"title":"On Black-Box Monitoring Techniques for Multi-Component Services","authors":"R. Filipe, Jaime Correia, Filipe Araújo, Jorge S. Cardoso","doi":"10.1109/NCA.2018.8548336","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548336","url":null,"abstract":"Despite the advantages of microservice and function-oriented architectures, there is an increase in complexity to monitor such highly dynamic systems. In this paper, we analyze two distinct methods to tackle the monitoring problem in a system with reduced instrumentation. Our goal is to understand the feasibility of such approach with one specific driver: simplicity. We aim to determine the extent to which it is possible to characterize the state of two generic tandem processes, using as little information as possible. To answer this question, we resorted to a simulation approach. Using a queue system, we simulated two services, that we could manipulate with distinct operation sets for each module. We used the total response time seen upstream of the system. Having this setup and metric, we applied two distinct methods to analyze the results. First, we used supervised machine learning algorithms to identify where the bottleneck is happening. Secondly, we used an exponential decomposition to identify the occupation in the two components in a more black-box fashion. Results show that both methodologies have their advantages and limitations. The separation of the signal more accurately identifies occupation in low occupied resources, but when a service is totally dominating the overall time, it lacks precision. The machine learning has a more stable error, but needs the training set. This study suggest that a black-box occupation approach with both techniques is possible and very useful.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128806356","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: 2

Performance Modeling of Hyperledger Fabric (Permissioned Blockchain Network) Hyperledger Fabric (Permissioned Blockchain Network)性能建模

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548070

H. Sukhwani, Nan Wang, Kishor S. Trivedi, A. Rindos

{"title":"Performance Modeling of Hyperledger Fabric (Permissioned Blockchain Network)","authors":"H. Sukhwani, Nan Wang, Kishor S. Trivedi, A. Rindos","doi":"10.1109/NCA.2018.8548070","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548070","url":null,"abstract":"Hyperledger Fabric (HLF) is an open-source implementation of a distributed ledger platform for running smart contracts in a modular architecture. In this paper, we present a performance model of Hyperledger Fabric v1.0+ using Stochastic Reward Nets (SRN). From our detailed model, we can compute the throughput, utilization and mean queue length at each peer and critical processing stages within a peer. To validate our model, we setup an HLF network in our lab and run workload using Hyperledger Caliper. From our analysis results, we find that time to complete the endorsement process is significantly affected by the number of peers and policies such as AND (). The performance bottleneck of the ordering service and ledger write can be mitigated using a larger block size, albeit with an increase in latency. For the committing peer, the transaction validation check (using Validation System Chaincode (VSCC)) is a time-consuming step, but its performance impact can be easily mitigated since it can be parallelized. However, its performance is critical, since it absorbs the shock of bursty block arrivals. We also analyze various what-if scenarios, such as peers processing transactions in a pipeline, and multiple endorsers per organization.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006856","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: 123

Efficient and Robust WiFi Indoor Positioning Using Hierarchical Navigable Small World Graphs 基于分层可导航小世界图的高效鲁棒WiFi室内定位

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548076

Max Willian Soares Lima, Horácio Oliveira, E. Santos, E. Moura, R. Costa, M. Levorato

{"title":"Efficient and Robust WiFi Indoor Positioning Using Hierarchical Navigable Small World Graphs","authors":"Max Willian Soares Lima, Horácio Oliveira, E. Santos, E. Moura, R. Costa, M. Levorato","doi":"10.1109/NCA.2018.8548076","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548076","url":null,"abstract":"Indoor positioning systems consist of identifying the physical location of devices inside buildings. They are usually based on the signal strength of a device packet received by a set of WiFi access points. Among the most precise solutions, are those based on machine learning algorithms, such as kNN (k-Nearest Neighbors). This technique is known as fingerprint positioning. Even though kNN is one of the most used classification methods due to its high precision results, it lacks scalability since an instance we need to classify must be compared to all other instances in the training base. In this work, we use a novel hierarchical navigable small world graph technique to fit the training database so that the samples can be efficiently classified in the online phase of the fingerprint positioning, allowing it to be used in large-scale scenarios and/or to be executed in resource-limited devices. We evaluated the performance of this solution using both synthetic and real-world training data and compared its performance to other known kNN variants such as kd-tree and ball-tree. Our results clearly show the performance gains of the graph-based solution, while still being able to maintain or even reduce the positioning error.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126336935","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: 9

Estimating the Environmental Impact of Data Centers 评估数据中心的环境影响

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548326

João Ferreira, G. Callou, Albert Josua, P. Maciel

引用次数: 10

Deep Content: Unveiling Video Streaming Content from Encrypted WiFi Traffic 深度内容:从加密WiFi流量中揭示视频流内容

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548317

Ying Li, Yi Huang, R. Xu, Suranga Seneviratne, Kanchana Thilakarathna, A. Cheng, Darren Webb, Guillaume Jourjon

{"title":"Deep Content: Unveiling Video Streaming Content from Encrypted WiFi Traffic","authors":"Ying Li, Yi Huang, R. Xu, Suranga Seneviratne, Kanchana Thilakarathna, A. Cheng, Darren Webb, Guillaume Jourjon","doi":"10.1109/NCA.2018.8548317","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548317","url":null,"abstract":"The proliferation of smart devices has led to an exponential growth in digital media consumption, especially mobile video for content marketing. The vast majority of the associated Internet traffic is now end-to-end encrypted, and while encryption provides better user privacy and security, it has made network surveillance an impossible task. The result is an unchecked environment for exploiters and attackers to distribute content such as fake, radical and propaganda videos. Recent advances in machine learning techniques have shown great promise in characterising encrypted traffic captured at the end points. However, video fingerprinting from passively listening to encrypted traffic, especially wireless traffic, has been reported as a challenging task due to the difficulty in distinguishing retransmissions and multiple flows on the same link. We show the potential of fingerprinting videos by passively sniffing WiFi frames in air, even without connecting to the WiFi network. We have developed Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) that are able to identify streamed YouTube videos from a closed set, by sniffing WiFi traffic encrypted at both Media Access Control (MAC) and Network layers. We compare these models to the state-of-the-art wired traffic classifier based on Convolutional Neural Networks (CNNs), and show that our models obtain similar results while requiring significantly less computational power and time (approximately a threefold reduction).","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130849769","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: 32

How Kernel Randomization is Canceling Memory Deduplication in Cloud Computing Systems 在云计算系统中，内核随机化如何取消内存重复数据删除

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548338

Fernando Vano-Garcia, Héctor Marco-Gisbert

引用次数: 5

Monitoring IoT Networks for Botnet Activity 监控物联网网络的僵尸网络活动

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548330

S. Chawathe

引用次数: 18

Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples 基于敌对样本流和随机森林的僵尸网络检测器规避

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548327

Giovanni Apruzzese, M. Colajanni

引用次数: 45

Analytical Hierarchy Process Multi-Metric Objective Function for RPL RPL的层次分析法多度量目标函数

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548054

W. Alayed, L. Mackenzie, D. Pezaros

引用次数: 11

SAAC: Secure Android Application Context a Runtime Based Policy and its Architecture 基于运行时策略的安全Android应用上下文及其体系结构

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548343

Guillaume Averlant, E. Alata, M. Kaâniche, V. Nicomette, Yuxiao Mao

引用次数: 4