2020 29th International Conference on Computer Communications and Networks (ICCCN)最新文献_第5页

PDSProxy: Trusted IoT Proxies for Confidential Ad-hoc Personalization of AI Services PDSProxy:可信任的物联网代理，用于AI服务的机密自组织个性化

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209655

Christian Meurisch, Bekir Bayrak, Florian Giger, M. Mühlhäuser

引用次数: 4

User-Friendly Design of Cryptographically-Enforced Hierarchical Role-based Access Control Models 基于密码强制的分层角色访问控制模型的用户友好设计

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209656

Xiaoyuan Yu, Brandon Haakenson, Tyler Phillips, X. Zou

{"title":"User-Friendly Design of Cryptographically-Enforced Hierarchical Role-based Access Control Models","authors":"Xiaoyuan Yu, Brandon Haakenson, Tyler Phillips, X. Zou","doi":"10.1109/ICCCN49398.2020.9209656","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209656","url":null,"abstract":"Data access control is a critical issue for any organization generating, recording or leveraging sensitive information. The popular Role-based Access Control (RBAC) model is well- suited for large organizations with various groups of personnel, each needing their own set of data access privileges. Unfortunately, the traditional RBAC model does not involve the use of cryptographic keys needed to enforce access control policies and protect data privacy. Cryptography-based Hierarchical Access Control (CHAC) models, on the other hand, have been proposed to facilitate RBAC models and directly enforce data privacy and access controls through the use of key management schemes. Though CHAC models and efficient key management schemes can support large and dynamic organizations, they are difficult to design and maintain without intimate knowledge of symmetric encryption, key management and hierarchical access control models. Therefore, in this paper we propose an efficient algorithm which automatically generates a fine-grained CHAC model based on the input of a highly user-friendly representation of access control policies. The generated CHAC model, the dual-level key management (DLKM) scheme, leverages the collusion-resistant Access Control Polynomial (ACP) and Atallah’s Efficient Key Management scheme in order to provide privacy at both the data and user levels. As a result, the proposed model generation algorithm serves to democratize the use of CHAC. We analyze each component of our proposed system and evaluate the resulting performance of the user-friendly CHAC model generation algorithm, as well as the DLKM model itself, along several dimensions.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125473787","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

PDSProxy++: Proactive Proxy Deployment for Confidential Ad-hoc Personalization of AI Services PDSProxy++:用于AI服务的机密自组织个性化的主动代理部署

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209747

Christian Meurisch, Dennis Werner, Florian Giger, Bekir Bayrak, M. Mühlhäuser

{"title":"PDSProxy++: Proactive Proxy Deployment for Confidential Ad-hoc Personalization of AI Services","authors":"Christian Meurisch, Dennis Werner, Florian Giger, Bekir Bayrak, M. Mühlhäuser","doi":"10.1109/ICCCN49398.2020.9209747","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209747","url":null,"abstract":"Personal data stores (PDS) typically provide extensions for external confidential processing, allowing ad-hoc personalization of AI services on nearby (third-party) Internet of Things (IoT) devices. However, these extensions entail a high initialization overhead due to the underlying cryptographic mechanisms. While some approaches provide first optimizations by pre-initializing this confidential environment, it is unclear which devices need to be pre-initialized – too many unnecessary devices are inefficient, and ad-hoc initialization still takes too long, especially when the user is moving. In this paper, we tackle this initialization issue by proposing PDSProxy++—a PDS extension for proactive multi-hop deployment of AI services. Inspired by the human eye, PDSProxy++ is based on a central cone (foveal vision) and a surrounding smaller circle (peripheral vision), which determine the nearby IoT devices to be initialized. Using a city-wide, real-world smart street lamp dataset and emulations, we show the feasibility of PDSProxy++ and its efficiency: it outperforms the currently-practiced ad-hoc mode and other deployment baselines in different smart city scenarios.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121553032","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 and Prediction of Big Data Workflows: An Exploratory Analysis 大数据工作流的性能建模与预测:探索性分析

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209715

Wuji Liu, C. Wu, Qianwen Ye, Aiqin Hou, Wei Shen

{"title":"Performance Modeling and Prediction of Big Data Workflows: An Exploratory Analysis","authors":"Wuji Liu, C. Wu, Qianwen Ye, Aiqin Hou, Wei Shen","doi":"10.1109/ICCCN49398.2020.9209715","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209715","url":null,"abstract":"Many next-generation scientific and business applications feature large-scale data-intensive workflows, which require massive computing resources for execution on high-performance clusters in cloud environments. Such computing resources (e.g., VCores and virtual memory) requested through parameter setting in big data systems, if not fully utilized by workloads, are simply wasted due to the nature of exclusive access made possible by containerization. This necessitates accurate modeling and prediction of workflow performance to make an effective recommendation of appropriate parameter settings to end users. However, it is challenging to determine optimal workflow and system configurations due to the large parameter space and the interaction between various technology layers of big data systems. Towards this goal, we propose a machine learning-based feature selection method to identify influential parameters based on historical performance measurements of Spark-based computing workloads executed in big data systems with YARN. We first identify a comprehensive set of parameters across multiple layers in the big data technology stack including workflow input structure, Spark computing engine, and YARN resource management. We then conduct an in-depth exploratory analysis of their individual and coupled impact on workflow performance, and develop a performance-influence model using random forest for prediction. Experimental results show that the proposed approach identifies important features for performance modeling and achieves high accuracy in performance prediction.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124747764","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

Beyond Model-Level Membership Privacy Leakage: an Adversarial Approach in Federated Learning 超越模型级成员隐私泄露:联邦学习中的一种对抗方法

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209744

Jiale Chen, Jiale Zhang, Yanchao Zhao, Hao Han, Kun Zhu, Bing Chen

{"title":"Beyond Model-Level Membership Privacy Leakage: an Adversarial Approach in Federated Learning","authors":"Jiale Chen, Jiale Zhang, Yanchao Zhao, Hao Han, Kun Zhu, Bing Chen","doi":"10.1109/ICCCN49398.2020.9209744","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209744","url":null,"abstract":"With the rise of privacy concerns in traditional centralized machine learning services, the federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received signifi-cant attention in both industry and academia. However, recent researches reveal the inherent vulnerabilities of the federated learning for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the membership to a more confined level, user-level. In this paper, We propose a novel user-level inference attack mechanism in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of the federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single-label and multi-label.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130502428","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: 24

Adaptive and Low-cost Traffic Engineering based on Traffic Matrix Classification 基于流量矩阵分类的自适应低成本交通工程

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209641

Nan Geng, Mingwei Xu, Yuan Yang, Enhuan Dong, Chenyi Liu

{"title":"Adaptive and Low-cost Traffic Engineering based on Traffic Matrix Classification","authors":"Nan Geng, Mingwei Xu, Yuan Yang, Enhuan Dong, Chenyi Liu","doi":"10.1109/ICCCN49398.2020.9209641","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209641","url":null,"abstract":"Traffic engineering (TE) attracts extensive researches over the years. Operators expect to design a TE scheme which accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routings based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces small overhead of routing updates.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122283604","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

Research on Design and Application of Mobile Edge Computing Model Based on SDN 基于SDN的移动边缘计算模型设计与应用研究

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209601

Shaohua Cao, Zhihao Wang, Yizhi Chen, Dingde Jiang, Yang Yan, Hui Chen

{"title":"Research on Design and Application of Mobile Edge Computing Model Based on SDN","authors":"Shaohua Cao, Zhihao Wang, Yizhi Chen, Dingde Jiang, Yang Yan, Hui Chen","doi":"10.1109/ICCCN49398.2020.9209601","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209601","url":null,"abstract":"With the rapid development of the mobile Internet and the Internet of Things (IoT), the conventional centralized cloud computing environment is facing severe challenges, such as high latency, and low bandwidth which significantly reduces the user experience for the applications of Virtual Reality (VR), HD Video, etc. Mobile Edge Computing (MEC) architecture can shift several tasks to devices on the edge of the mobile network, decreasing the service time and relieving the flow pressure of the core network. Combining Software Defined Networking (SDN) and MEC, this paper proposes a MEC network model based on SDN and builds test models on physical devices. A set of network testing experiments is carried out to evaluate the performance of the topology. Meanwhile, motivated by the demand for quick processing of surveillance video, an intelligent video processing acceleration application is deployed on the testing platform and cloud computing platform. Under the control of a Floodlight controller, it shows that the MEC scheme proposed in this paper has better performance when carrying latency-sensitive services.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147065","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

Resource Management for Processing Wide Area Data Streams on Supercomputers 超级计算机上处理广域数据流的资源管理

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209669

Joaquín Chung, Mainak Adhikari, S. Srirama, Eun-Sung Jung, R. Kettimuthu

{"title":"Resource Management for Processing Wide Area Data Streams on Supercomputers","authors":"Joaquín Chung, Mainak Adhikari, S. Srirama, Eun-Sung Jung, R. Kettimuthu","doi":"10.1109/ICCCN49398.2020.9209669","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209669","url":null,"abstract":"Modern scientific instruments generate enormous amount of data. Typically, the data collected from the instruments are stored in one or more files that are then moved to a distant supercomputer for processing. The final results are sent back to the user. In order to make effective use of the time on expensive instruments, experimenters want to process the data as they are generated. They want to stream the data from instruments’ memory directly to a supercomputer’s memory for analysis. Since the compute nodes in a supercomputer are not connected directly to the wide area network, the data streams need to be passed through intermediate gateway nodes. As opposed to the best effort file transfers, data streaming applications require resources at a specific time for a specific period. In this paper, we present a system model for enabling data streaming through gateway nodes and an algorithm to efficiently allocate gateway node resources along with compute nodes. We evaluate the algorithm using real-world traces on the Chameleon Cloud. The results show that our system can schedule compute and gateway resources efficiently for streaming analysis.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129681256","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

Using Trust as a Measure to Derive Data Quality in Data Shared IoT Deployments 在数据共享物联网部署中使用信任作为导出数据质量的措施

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209633

John Byabazaire, G. O’hare, D. Delaney

{"title":"Using Trust as a Measure to Derive Data Quality in Data Shared IoT Deployments","authors":"John Byabazaire, G. O’hare, D. Delaney","doi":"10.1109/ICCCN49398.2020.9209633","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209633","url":null,"abstract":"Recent developments in Internet of Things have heightened the need for data sharing across application domains to foster innovation. As most of these IoT deployments are based on heterogeneous sensor types, there is increased scope for sharing erroneous, inaccurate or inconsistent data. This in turn may lead to inaccurate models built from this data. It is important to evaluate this data as it is collected to establish its quality. This paper presents an analysis of data quality as it is represented in Internet of Things (IoT) systems and some of the limitations of this representation. The paper then introduces the use of trust as a heuristic to drive data quality measurements. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd sourced or other unreliable data collection techniques. The analysis extends to detail an appropriate framework for representing data quality within the big data model. To demonstrate the application of a trust backed framework, we used data collected from a IoT deployment of sensors to measure air quality in which a low cost sensor was co-located with a gold reference sensor. Using data streams modeled based on a dataset from an IoT deployment, our initial results show that the framework’s trust score are consistent with the accuracy measure of the machine learning models.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550531","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

FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers FT-VMP:云数据中心中的容错虚拟机布局

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209676

Christopher Gonzalez, Bin Tang

{"title":"FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers","authors":"Christopher Gonzalez, Bin Tang","doi":"10.1109/ICCCN49398.2020.9209676","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209676","url":null,"abstract":"Virtual machine (VM) replication is an effective technique in cloud data centers to achieve fault-tolerance, load-balance, and quick-responsiveness to user requests. In this paper we study a new fault-tolerant VM placement problem referred to as FT-VMP. Given that different VM has different fault-tolerance requirement (i.e., difference VM requires different number of replica copies) and compatibility requirement (i.e., some VMs and their replicas cannot be placed into some physical machines (PMs) due to software or platform incompatibility), FT-VMP studies how to place VM replica copies inside cloud data centers in order to minimize the number of PMs storing VM replicas, under the constraints that i) for fault-tolerant purpose, replica copies of the same VM cannot be placed inside the same PM and ii) each PM has a limited amount of storage capacity. We first prove that FT-VMP is NP-hard. We then design an integer linear programming (ILP)-based algorithm to solve it optimally. As ILP takes time to compute thus is not suitable for large scale cloud data centers, we design a suite of efficient and scalable heuristic fault-tolerant VM placement algorithms. We show that a) ILP-based algorithm outperforms the state-of-the-art VM replica placement in a wide range of network dynamics and b) that all our fault-tolerant VM placement algorithms are able to turn off significant number of PMs to save energy in cloud data centers. In particular, we show that our algorithms can consolidate (i.e., turn off) around 100 PMs in a small data center of 256 PMs and 700 PMs in a large data center of 1028PMs.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398144","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