2022 IEEE 11th International Conference on Cloud Networking (CloudNet)最新文献

Token-based authentication and access delegation for HW-accelerated telco cloud solution 基于令牌的hw加速电信云解决方案认证和访问授权

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978865

Semih Ince, D. Espès, G. Gogniat, R. Santoro, Julien Lallet

{"title":"Token-based authentication and access delegation for HW-accelerated telco cloud solution","authors":"Semih Ince, D. Espès, G. Gogniat, R. Santoro, Julien Lallet","doi":"10.1109/CloudNet55617.2022.9978865","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978865","url":null,"abstract":"Telecommunications networks evolve towards cloud computing. The vast deployment of heavy processing applications (e.g., AI/ML) in telco has introduced performance constraints. The recent emergence of FPGA-based clouds brings new opportunities for telco services to guarantee a good quality of service and performance. In a telco context as in a cloud context, security is critical. Telecom Operator (TO) privacy and data protection must be ensured in FPGA-based cloud solutions. First, the method used to remotely access the FPGA cloud must be secure. Then, each TO’s execution context has to be isolated to protect user privacy and confidentiality. For that, authorizations must be defined and access control policies need to be properly enforced. In this paper, an authentication and tokenized FPGA access delegation scheme adapted from OAuth 2 is described. This solution brings a new token-based approach for FPGA devices and proposes a secure access delegation protocol for the cloudification of 5G networks and beyond. The access token is a lightweight solution to securely share information and enforce access control inside a multi-user FPGA. The proposed solution allows a TO to securely share third party cloud resources with another TO subcontractor. The sharing process involves a Trusted Authority (TA) and works without the approval of the FPGA cloud owner. This mechanism and the associated FPGA environment reinforces the highly sought privacy and confidentiality in FPGA clouds. FPGA resource allocation and reconfiguration is achieved in 503 ms and is 37.8% faster when compared to the literature. Performance results show flexibility and cost efficiency for telco services.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057854","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

CloudNet 2022 Cover Page CloudNet 2022封面页

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/cloudnet55617.2022.9978823

引用次数: 0

Optimal Network Selection Method Using Federated Learning to Achieve Large-Scale Learning While Preserving Privacy 利用联邦学习实现大规模学习同时保护隐私的最优网络选择方法

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978891

Koki Horita, Bin Yang, T. Carette, Masanobu Jimbo, A. Nakao

{"title":"Optimal Network Selection Method Using Federated Learning to Achieve Large-Scale Learning While Preserving Privacy","authors":"Koki Horita, Bin Yang, T. Carette, Masanobu Jimbo, A. Nakao","doi":"10.1109/CloudNet55617.2022.9978891","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978891","url":null,"abstract":"These days, smartphones are equipped with multiple radio access technologies (RAT) such as wireless LAN (WLAN) and 4G/5G cellular with autonomous switching between them. However, it is often the case with RAT switching algorithms being based on availability but not on predicted quality of communications, which poses significant frustrations on the part of users, especially when their smartphones connect to poor-quality WLAN automatically. In order to provide uninterrupted communication to smartphone users, it is necessary to predict WLAN quality before switching networks and change the network based on the result. Although machine learning approaches are useful for predicting network quality, conventional machine learning is infeasible to train on large amounts of data collected from users. It is because WLAN data contain private information such as user location information, and it is possible to track user behavior from the training data. In addition, employing complex machine learning algorithms is not possible with the computational resources of a smartphone due to constraints on training and inference time. To the best of our knowledge, no paper implements and evaluates a solution to this problem. To solve this issue, this paper utilizes latent class regression model trained by Federated Learning. Our proposal achieves on-device training on a smartphone with limited computer resource and protects users’ private information. It predicts the quality of access points with various characteristics with high accuracy (95.0% precision, 23.7% recall). Moreover, by developing and installing the application on an Android smartphone, we show that the proposed method reduces the actual application communication disruption time to within 3 seconds at the 75th percentile (85% reduction compared to no functionality) and completes training within 1 second per session on smartphones with limited computational resources.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115918393","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

Message from the IEEE CloudNet 2022 Chairs 来自IEEE CloudNet 2022主席的消息

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/cloudnet55617.2022.9978845

引用次数: 0

An Enhanced Cloud-Native Deep Learning Pipeline for Network Traffic Classification 一种用于网络流量分类的增强云原生深度学习管道

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978774

Ahmed Elkenawy, S. Aly

{"title":"An Enhanced Cloud-Native Deep Learning Pipeline for Network Traffic Classification","authors":"Ahmed Elkenawy, S. Aly","doi":"10.1109/CloudNet55617.2022.9978774","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978774","url":null,"abstract":"Cloud-native computing approaches can be used with deep learning techniques to offer enhanced solutions to real-world problems. Given that much reliance on modern technology has emerged as a substitute to face to face interaction, deep learning techniques can be very effective in supporting modern services such as the classification of network traffic. Such classification can have many purposes like quality-of-service provisioning, security, and the detection of unauthorized traffic. Deep learning, however, requires lots of extensive experimentation and plenty of data that are labeled. In this paper, we propose an enhanced cloud-native deep learning pipeline to mitigate these problems and use network traffic classification as a use case. We use a cloud-native serverless approach for data pre-processing, model building (hyperparameter tuning), and model serving. Our approach also supports fully and partially labeled data scenarios. We were able to achieve speedup and scalability values that are near to theoretically calculated ones. Furthermore, in a limited time budget, our approach reached better accuracy compared to existing work. Our paper defines the problem, surveys background work that attempt to use various approaches, presents the proposed approach, and finally describes the experiments and associated results.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128236166","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

Cutting Edge: Privacy Analysis of the ETSI MEC APIs and the Potential Vulnerabilities 前沿:ETSI MEC api的隐私分析和潜在漏洞

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978741

Daniel Fraunholz, Richard Schörghofer-Vrinssen, H. König, Daniel Reti

{"title":"Cutting Edge: Privacy Analysis of the ETSI MEC APIs and the Potential Vulnerabilities","authors":"Daniel Fraunholz, Richard Schörghofer-Vrinssen, H. König, Daniel Reti","doi":"10.1109/CloudNet55617.2022.9978741","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978741","url":null,"abstract":"New cellular networks like 5G and 6G put novel methods and technologies forth which sustainably change the mobile communication domain. Edge cloud computing is one of these emerging technologies providing computational resources at the edge of the network in closer proximity to end users. It promises significantly reduced network latencies, better load balancing, and more precise optimizations through information about the states of the access networks provided by the cloud infrastructure, e.g., radio conditions, location information, and others. The European Telecommunications Standards Institute (ETSI) has summarized and standardized the requirements for a multi-access edge computing (MEC) architecture as basis for future implementations. Among others, the standard defines a large number of interfaces between the various components and to the environment. It also formulates security requirements for these interfaces. However, the implementation details are usually left to the platform operators. In this paper, we present an in-depth analysis of the privacy properties of these interfaces and point out several potential privacy vulnerabilities. We describe three possible attacks that exploit these vulnerabilities. For our analysis, we use a simulative edge computing sandbox environment that has been developed by ETSI to investigate the interfaces. As a conclusion, we formulate recommendations on how data protection requirements can be met in future MEC implementations.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"2743 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127486538","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

On the Deployment of Blockchain in Edge Computing Wireless Networks 区块链在边缘计算无线网络中的部署研究

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978739

Wael Jaafar, Koutoua Jean Romeo Beyara, I. Aouini, Jihene Ben Abderrazak, H. Yanikomeroglu

{"title":"On the Deployment of Blockchain in Edge Computing Wireless Networks","authors":"Wael Jaafar, Koutoua Jean Romeo Beyara, I. Aouini, Jihene Ben Abderrazak, H. Yanikomeroglu","doi":"10.1109/CloudNet55617.2022.9978739","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978739","url":null,"abstract":"Blockchain is the core technology at the heart of cryptocurrency such as Bitcoin and Ether. Due to its characteristics, e.g., immutability, decentrality, and consensus, it is now applied in several fields such as communications, e-health, and supply chains. Nevertheless, its integration to communication networks is facing several challenges related to power consumption, unreliable communication channels, and limited computing capacity of devices. In this paper, we focus on blockchain consensus optimization in edge computing wireless networks. Specifically, we propose a novel consensus mechanism adapted to the heterogeneity and dynamicity, e.g., mobility, of edge computing nodes. We call it adaptive blockchain for edge computing (ABEC). Through experiments, we demonstrate the superiority of ABEC over baseline consensus, in terms of block addition/confirmation latency and robustness to the edge nodes’ unreliable behaviour. Finally, the impact of several network parameters, e.g., data rate of master ruler/block’s owner, and number of transactions per block, is investigated.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132772515","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

Development of a Secure Data Sharing Mechanism for IoT Application Systems 物联网应用系统安全数据共享机制研究

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978835

Naoya Kitagawa, A. Takefusa, Kento Aida

{"title":"Development of a Secure Data Sharing Mechanism for IoT Application Systems","authors":"Naoya Kitagawa, A. Takefusa, Kento Aida","doi":"10.1109/CloudNet55617.2022.9978835","DOIUrl":"https://doi.org/10.1109/CloudNet55617.2022.9978835","url":null,"abstract":"Research on Internet of Things (IoT) application systems, which store data collected by various sensor devices to a cloud and utilize the data through statistical analysis and machine learning, has been conducted extensively. Several platforms have been proposed to support the development of IoT application systems, which enable system administrators to easily manage edge hardware and group sensors. However, conventional development support platforms do not provide functions for secure data management, such as encryption processing when handling confidential information and flexible access control among members of a research group. In this paper, we propose a mechanism for securely and easily encrypting data and sharing data using a Secure Configuration Server. We have been developing \"SINETStream,\" an IoT application development support tool provided by National Institute of Informatics, Japan. The secure configuration server can co-work with the SINETStream-based IoT systems. This mechanism enables easy sharing of confidential information by securely managing data encryption keys within the system, which has been complicated with conventional methods. In addition, our mechanism provides users with confidential information in an encrypted form, so that only authorized users can view the data.","PeriodicalId":260541,"journal":{"name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","volume":"35 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114002780","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

Extreme Bitmapping: Efficiently Sorting Data for Cloud Compression 极端位图:有效地排序云压缩数据

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978780

Christian Mørup, Anders Lindskov Kloborg, D. Lucani

引用次数: 0

MORA on the Edge: a testbed of Multiple Option Resource Allocation 边缘上的MORA:多选项资源分配的测试平台

2022 IEEE 11th International Conference on Cloud Networking (CloudNet) Pub Date : 2022-11-07 DOI: 10.1109/CloudNet55617.2022.9978894

W. Ouedraogo, A. D. Stefano, Andrea Araldo, A. Stefano

引用次数: 0