2018 IEEE/ACM Symposium on Edge Computing (SEC)最新文献_第2页

HomePad: A Privacy-Aware Smart Hub for Home Environments HomePad:家庭环境中具有隐私意识的智能中心

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00012

Igor Zavalyshyn, N. Duarte, Nuno Santos

引用次数: 20

Enabling Deep Learning on IoT Edge: Approaches and Evaluation 在物联网边缘实现深度学习:方法和评估

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00047

Xuan Qi, Chen Liu

引用次数: 26

Mobile Edge Computing – a Booster for the Practical Provisioning Approach of Web-Based Augmented Reality 移动边缘计算——基于web的增强现实的实用配置方法的助推器

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00041

Pei Ren, Xiuquan Qiao, Junliang Chen, S. Dustdar

引用次数: 38

A Deep Neural Network Compression Algorithm Based on Knowledge Transfer for Edge Device 基于知识转移的边缘设备深度神经网络压缩算法

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00035

Chao Li, Xiaolong Ma, Zhulin An, Yongjun Xu

引用次数: 4

Portable Energy-Aware Cluster-Based Edge Computers 便携式能量感知集群边缘计算机

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00026

T. Rausch, Cosmin Avasalcai, S. Dustdar

{"title":"Portable Energy-Aware Cluster-Based Edge Computers","authors":"T. Rausch, Cosmin Avasalcai, S. Dustdar","doi":"10.1109/SEC.2018.00026","DOIUrl":"https://doi.org/10.1109/SEC.2018.00026","url":null,"abstract":"Computational resources distributed at the edge of the network are the fundamental infrastructural component of edge computing. The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. Many application scenarios require edge computing resources to provide reliable response times while operating in dynamic and resource-constrained environments. In this paper, we present a novel architecture for energy-aware, cluster-based edge computers that are designed to be portable and usable in fieldwork scenarios. We use compact general-purpose commodity hardware to build a high-density cluster prototype, and implement a power-management runtime to enable real-time energy-awareness. Furthermore, we present an experimental analysis of the energy and resource-consumption characteristics of our prototype in the context of a data analytics application. The results show the feasibility of our prototype for the presented scenarios, but also reveal the intricacies of power-management approaches already built into modern CPUs. We show that different load balancing policies and cluster configurations have a significant impact on energy consumption and system responsiveness. Our insights lay the groundwork for future research on energy-consumption optimization approaches for cluster-based edge computers.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"38 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":"131357292","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

QoE Inference and Improvement Without End-Host Control 无终端主机控制的QoE推断与改进

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00011

Ashkan Nikravesh, Qi Alfred Chen, Scott Haseley, Xiao Zhu, Geoffrey Challen, Z. Morley Mao

{"title":"QoE Inference and Improvement Without End-Host Control","authors":"Ashkan Nikravesh, Qi Alfred Chen, Scott Haseley, Xiao Zhu, Geoffrey Challen, Z. Morley Mao","doi":"10.1109/SEC.2018.00011","DOIUrl":"https://doi.org/10.1109/SEC.2018.00011","url":null,"abstract":"Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"21 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":"116500130","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: 11

Lightweight Hardware Based Secure Authentication Scheme for Fog Computing 基于轻量级硬件的雾计算安全认证方案

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00059

Baiyi Huang, Xiuzhen Cheng, Yuan Cao, Le Zhang

引用次数: 8

EdgeNet: A Global Cloud That Spreads by Local Action EdgeNet:通过地方行动传播的全球云

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00045

Justin Cappos, Matt Hemmings, R. McGeer, Albert Rafetseder, Glenn Ricart

{"title":"EdgeNet: A Global Cloud That Spreads by Local Action","authors":"Justin Cappos, Matt Hemmings, R. McGeer, Albert Rafetseder, Glenn Ricart","doi":"10.1109/SEC.2018.00045","DOIUrl":"https://doi.org/10.1109/SEC.2018.00045","url":null,"abstract":"EdgeNet has been informed by the advances of cloud computing and the successes of such distributed systems as PlanetLab, GENI, G-Lab, SAVI, and V-Node: a large number of small points-of-presence, designed for the deployment of highly distributed experiments and applications. EdgeNet differs from its predecessors in two significant areas: first, it is a software-only infrastructure, where each worker node is designed to run part-or full-time on existing hardware at the local site; and, second, it uses modern, industry-standard software both as the node agent and the control framework. The first innovation permits rapid and unlimited scaling: whereas GENI and PlanetLab required the installation and maintenance of dedicated hardware at each site, EdgeNet requires only a software download, and a node can be added to the EdgeNet infrastructure in 15 minutes. The second offers performance, maintenance, and training benefits; rather than maintaining bespoke kernels and control frameworks, and developing training materials on using the latter, we are able to ride the wave of open-source and industry development, and the plethora of industry and community tutorial materials developed for industry standard control frameworks. The result is a global Kubernetes cluster, where pods of Docker containers form the service instances at each point of presence.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"4 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":"114360901","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

MUVR: Supporting Multi-User Mobile Virtual Reality with Resource Constrained Edge Cloud MUVR:支持资源受限边缘云的多用户移动虚拟现实

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00008

Yong Li, Wei Gao

{"title":"MUVR: Supporting Multi-User Mobile Virtual Reality with Resource Constrained Edge Cloud","authors":"Yong Li, Wei Gao","doi":"10.1109/SEC.2018.00008","DOIUrl":"https://doi.org/10.1109/SEC.2018.00008","url":null,"abstract":"Virtual Reality (VR) fundamentally improves the user's experience when interacting with the virtual world, and could revolutionarily transform designs of many interactive systems. To provide VR from untethered mobile devices, a viable solution is to remotely render VR frames from the edge cloud, but encounters challenges from the limited computation and communication capacities of the edge cloud when serving multiple mobile VR users at the same time. In this paper, we envision the key reason of such challenges as the ignorance of redundancy across VR frames being rendered, and aim to fundamentally remove this performance constraint on highly dynamic VR applications by adaptively reusing the redundant VR frames being rendered for different VR users. Such redundancy in each frame is decided at run-time by the edge cloud, which is then able to memoize the previous results of VR frame rendering for future reuse by other users. After a VR frame is generated, the edge cloud further reuses its redundant pixels compared with other frames, and only transmits the distinct portion of this frame to mobile devices. We have implemented our design over Android OS and Unity VR application engine, and demonstrated that our design can efficiently reduce the computation burden at the edge cloud by more than 90%, and reduce more than 95% of the VR frame data being transmitted to mobile devices.","PeriodicalId":376439,"journal":{"name":"2018 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"73 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":"127803832","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: 44

Application-Aware IoT Camera Virtualization for Video Analytics Edge Computing 面向视频分析边缘计算的应用感知物联网摄像机虚拟化

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI: 10.1109/SEC.2018.00017

Si Young Jang, Yoonhyung Lee, B. Shin, Dongman Lee

引用次数: 39