2018 IEEE/ACM Symposium on Edge Computing (SEC)最新文献_第3页

Stable Clustering for VANETs on Highways 高速公路vanet的稳定聚类

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00053

Xiaolu Cheng, Baohua Huang, Wei Cheng

引用次数: 12

Demo: Managing Sensing Resources at the Edge using Cloud OSes 演示:使用云操作系统管理边缘感知资源

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00032

L. Osmani, Ashwin Rao, Samu Varjonen, Eemil Lagerspetz, H. Flinck, S. Tarkoma

引用次数: 0

Title Page i 第1页

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/sec.2018.00001

引用次数: 0

Preliminary Study of Trusted Execution Environments on Heterogeneous Edge Platforms 异构边缘平台可信执行环境的初步研究

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00057

Zhenyu Ning, Jinghui Liao, Fengwei Zhang, Weisong Shi

{"title":"Preliminary Study of Trusted Execution Environments on Heterogeneous Edge Platforms","authors":"Zhenyu Ning, Jinghui Liao, Fengwei Zhang, Weisong Shi","doi":"10.1109/SEC.2018.00057","DOIUrl":"https://doi.org/10.1109/SEC.2018.00057","url":null,"abstract":"The recent edge computing infrastructure introduces a new computing model that works as a complement of the traditional cloud computing. The edge nodes in the infrastructure reduce the network latency of the cloud computing model and increase data privacy by offloading the sensitive computation from the cloud to the edge. Recent research focuses on the applications and performance of the edge computing, but less attention is paid to the security of this new computing paradigm. Inspired by the recent move of hardware vendors that introducing hardware-assisted Trusted Execution Environment (TEE), we believe applying these TEEs on the edge nodes would be a natural choice to secure the computation and sensitive data on these nodes. In this paper, we investigate the typical hardware-assisted TEEs and evaluate the performance of these TEEs to help analyze the feasibility of deploying them on the edge platforms. Our experiments show that the performance overhead introduced by the TEEs is low, which indicates that integrating these TEEs into the edge nodes can efficiently mitigate security loopholes with a low-performance overhead.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"48 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":"122576816","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: 20

Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe 基于主题的发布/订阅中低延迟数据传播的可扩展边缘计算

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00023

S. Khare, Hongyang Sun, Kaiwen Zhang, Julien Gascon-Samson, A. Gokhale, X. Koutsoukos, Hamzah Abdelaziz

{"title":"Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe","authors":"S. Khare, Hongyang Sun, Kaiwen Zhang, Julien Gascon-Samson, A. Gokhale, X. Koutsoukos, Hamzah Abdelaziz","doi":"10.1109/SEC.2018.00023","DOIUrl":"https://doi.org/10.1109/SEC.2018.00023","url":null,"abstract":"Advances in Internet of Things (IoT) give rise to a variety of latency-sensitive, closed-loop applications that reside at the edge. These applications often involve a large number of sensors that generate volumes of data, which must be processed and disseminated in real-time to potentially a large number of entities for actuation, thereby forming a closed-loop, publish-process-subscribe system. To meet the response time requirements of such applications, this paper presents techniques to realize a scalable, fog/edge-based broker architecture that balances data publication and processing loads for topic-based, publish-process-subscribe systems operating at the edge, and assures the Quality-of-Service (QoS), specified as the 90th percentile latency, on a per-topic basis. The key contributions include: (a) a sensitivity analysis to understand the impact of features such as publishing rate, number of subscribers, per-sample processing interval and background load on a topic's performance; (b) a latency prediction model for a set of co-located topics, which is then used for the latency-aware placement of topics on brokers; and (c) an optimization problem formulation for k-topic co-location to minimize the number of brokers while meeting each topic's QoS requirement. Here, k denotes the maximum number of topics that can be placed on a broker. We show that the problem is NP-hard for k >=3 and present three load balancing heuristics. Empirical results are presented to validate the latency prediction model and to evaluate the performance of the proposed heuristics.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"34 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":"125435194","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: 15

Scalability and Performance Evaluation of Edge Cloud Systems for Latency Constrained Applications 延迟受限应用中边缘云系统的可扩展性和性能评估

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00028

S. Maheshwari, D. Raychaudhuri, I. Seskar, F. Bronzino

{"title":"Scalability and Performance Evaluation of Edge Cloud Systems for Latency Constrained Applications","authors":"S. Maheshwari, D. Raychaudhuri, I. Seskar, F. Bronzino","doi":"10.1109/SEC.2018.00028","DOIUrl":"https://doi.org/10.1109/SEC.2018.00028","url":null,"abstract":"This paper presents an analysis of the scalability and performance of an edge cloud system designed to support latency-sensitive applications. A system model for geographically dispersed edge clouds is developed by considering an urban area such as Chicago and co-locating edge computing clusters with known Wi-Fi access point locations. The model also allows for provisioning of network bandwidth and processing resources with specified parameters in both edge and the cloud. The model can then be used to determine application response time (sum of network delay, compute queuing and compute processing time), as a function of offered load for different values of edge and core compute resources, and network bandwidth parameters. Numerical results are given for the city-scale scenario under consideration to show key system level trade-offs between edge cloud and conventional cloud computing. Alternative strategies for routing service requests to edge vs. core cloud clusters are discussed and evaluated. Key conclusions from the study are: (a) the core cloud-only system outperforms the edge-only system having low inter-edge bandwidth, (b) a distributed edge cloud selection scheme can approach the global optimal assignment when the edge has sufficient compute resources and high inter-edge bandwidth, and (c) adding capacity to an existing edge network without increasing the inter-edge bandwidth contributes to network wide congestion and can reduce system capacity.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","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":"128498334","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: 89

Costless: Optimizing Cost of Serverless Computing through Function Fusion and Placement 无成本:通过功能融合和布局优化无服务器计算的成本

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00029

Tarek Elgamal, Atul Sandur, K. Nahrstedt, G. Agha

{"title":"Costless: Optimizing Cost of Serverless Computing through Function Fusion and Placement","authors":"Tarek Elgamal, Atul Sandur, K. Nahrstedt, G. Agha","doi":"10.1109/SEC.2018.00029","DOIUrl":"https://doi.org/10.1109/SEC.2018.00029","url":null,"abstract":"Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. However, since serverless platforms are relatively new, they have a completely different pricing model that depends on the memory, duration, and the number of executions of a sequence/workflow of functions. In this paper we present an algorithm that optimizes the price of serverless applications in AWS Lambda. We first describe the factors affecting price of serverless applications which include: (1) fusing a sequence of functions, (2) splitting functions across edge and cloud resources, and (3) allocating the memory for each function. We then present an efficient algorithm to explore different function fusion-placement solutions and find the solution that optimizes the application's price while keeping the latency under a certain threshold. Our results on image processing workflows show that the algorithm can find solutions optimizing the price by more than 35%-57% with only 5%-15% increase in latency. We also show that our algorithm can find non-trivial memory configurations that reduce both latency and price.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","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":"130745607","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: 98

KLRA: A Kernel Level Resource Auditing Tool For IoT Operating System Security KLRA:物联网操作系统安全的内核级资源审计工具

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00058

Dong Li, Zhaonian Zhang, W. Liao, Zhiwei Xu

{"title":"KLRA: A Kernel Level Resource Auditing Tool For IoT Operating System Security","authors":"Dong Li, Zhaonian Zhang, W. Liao, Zhiwei Xu","doi":"10.1109/SEC.2018.00058","DOIUrl":"https://doi.org/10.1109/SEC.2018.00058","url":null,"abstract":"Nowadays, the rapid development of the Internet of Things facilitates human life and work, while it also brings great security risks to the society due to the frequent occurrence of various security issues. IoT device has the characteristics of large-scale deployment and single responsibility application, which makes it easy to cause a chain reaction and results in widespread privacy leakage and system security problems when the software vulnerability is identified. It is difficult to guarantee that there is no security hole in the IoT operating system which is usually designed for MCU and has no kernel mode. An alternative solution is to identify the security issues in the first time when the system is hijacked and suspend the suspicious task before it causes irreparable damage. This paper proposes KLRA (A Kernel Level Resource Auditing Tool) for IoT Operating System Security This tool collects the resource-sensitive events in the kernel and audit the the resource consumption pattern of the system at the same time. KLRA can take fine-grained events measure with low cost and report the relevant security warning in the first time when the behavior of the system is abnormal compared with daily operations for the real responsibility of this device. KLRA enables the IoT operating system for MCU to generate the security early warning and thereby provides a self-adaptive heuristic security mechanism for the entire IoT system.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"44 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":"130575486","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

Privacy Partition: A Privacy-Preserving Framework for Deep Neural Networks in Edge Networks 隐私分区:边缘网络中深度神经网络的隐私保护框架

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00049

Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Yiming Liu, Haodong Liu, Jiasen Chen, Swee Sim, Vibha Iyengar, P. Tague, Yuan Tian

{"title":"Privacy Partition: A Privacy-Preserving Framework for Deep Neural Networks in Edge Networks","authors":"Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Yiming Liu, Haodong Liu, Jiasen Chen, Swee Sim, Vibha Iyengar, P. Tague, Yuan Tian","doi":"10.1109/SEC.2018.00049","DOIUrl":"https://doi.org/10.1109/SEC.2018.00049","url":null,"abstract":"The rise of the Internet of Things (IoT) encourages an emerging computing paradigm - edge computing - which leverages innovations in \"last mile\" communications infrastructure to provide improved quality of service guarantees to compute-intensive services such as autonomous driving and improved support for connected devices. Many high-value edge computing applications benefit from an integration of privacy-sensitive resource-constrained local data streams and data-hungry resource-constrained analytic tools like deep neural networks. We propose a practical method for privacy-preservation in deep learning classification tasks based on bipartite topology threat modeling and an interactive adversarial deep network construction in the context of edge computing. We term this approach Privacy Partition. A bipartite topology consisting of a trusted local partition and untrusted remote partition provides an apt alternative to centralized and federated collaborative deep learning frameworks in the case of deployment contexts such as IoT smart spaces, where users would like to restrict access to high-resolution data streams due to privacy concerns but would still like to benefit from deep learning services and external computational resources such as remote cloud data centers.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"15 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":"132040907","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: 15

How Edge Computing and Initial Congestion Window Affect Latency of Web-Based Services: Early Experiences with Baidu? 边缘计算和初始拥塞窗口如何影响web服务的延迟:百度的早期经验?

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00052

Qingyang Zhang, Hong Zhong, Jiaoren Wu, Weisong Shi

引用次数: 5