2018 IEEE International Conference on Edge Computing (EDGE)最新文献_第2页

EDGESTORE: A Single Namespace and Resource-Aware Federation File System for Edge Servers EDGESTORE:用于边缘服务器的单一命名空间和资源感知的联合文件系统

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00021

Awais Khan, M. Attique, Youngjae Kim, Sungyong Park, Byungchul Tak

{"title":"EDGESTORE: A Single Namespace and Resource-Aware Federation File System for Edge Servers","authors":"Awais Khan, M. Attique, Youngjae Kim, Sungyong Park, Byungchul Tak","doi":"10.1109/EDGE.2018.00021","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00021","url":null,"abstract":"With the increasing adoption of edge computing, the capacity requirements of the edge servers are also growing. Especially the data volume generated from a large number of edge clients and/or edge devices demand more capacity to be able to store them for processing. The growing gap between the data volume and current storage capacity is motivating the need towards building aggregated storage spaces. Aggregated storage can be an effective way to extend edge servers' overall storage capacity by combining storage resources of other nodes under the agreement to share. Several Federation file systems exist to meet this aggregate storage needs but are not without limitations. Dependency to the specific software stack makes it unfit for general-purpose use and they often neglect important features critical for the performance. In this paper, we address the important challenges of building the Federation on top of edge servers with the heterogeneous file system and resource configurations. We prototyped EDGESTORE, a Federation File System for Edge Servers. EDGESTORE equips the users with an aggregate storage namespace and federates resources of edge servers, to enable high resource-sharing in Federation. We propose, Job and Resource-Aware Request Placement algorithm (JRAP) to take advantage of edge server resource heterogeneity. To evaluate the usefulness of EDGESTORE, we consider two federation scenarios i) with same resource configurations and ii) with different resource configurations. We evaluate the efficacy of various big data applications from data storage to analysis using EDGESTORE on a real testbed.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133106","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: 8

Data Distillation at the Network's Edge: Exposing Programmable Logic with InLocus 网络边缘的数据蒸馏:用InLocus暴露可编程逻辑

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00011

Lucas R. B. Brasilino, A. Shroyer, Naveen Marri, Saurabh Agrawal, Catherine L. Pilachowski, E. Kissel, D. M. Swany

引用次数: 5

Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies 边缘动力工业控制:云与自动化技术相结合的概念

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00026

Christoph Pallasch, S. Wein, Nicolai Hoffmann, M. Obdenbusch, Tilman Buchner, J. Waltl, C. Brecher

引用次数: 27

An Energy-Aware Edge Server Placement Algorithm in Mobile Edge Computing 移动边缘计算中的能量感知边缘服务器放置算法

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00016

Yuanzhen Li, Shangguang Wang

引用次数: 158

SaRa: A Stochastic Model to Estimate Reliability of Edge Resources in Volunteer Cloud 基于随机模型的志愿者云边缘资源可靠性评估

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00024

Yousef S. Alsenani, G. Crosby, Tomas Velasco

{"title":"SaRa: A Stochastic Model to Estimate Reliability of Edge Resources in Volunteer Cloud","authors":"Yousef S. Alsenani, G. Crosby, Tomas Velasco","doi":"10.1109/EDGE.2018.00024","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00024","url":null,"abstract":"With the increasing popularity and the need for low-cost green computing systems, new paradigms and models such as fog, edge, and volunteer cloud computing (e.g. cuCloud) have recently emerged. cuCloud, one of the appealing volunteer cloud computing system, share the same philosophy as desktop grid, which runs on underutilized and or spare resources of personal computers (i.e. volunteer hosts) owned by individuals and organizations. On one side of the spectrum, underlying cuCloud infrastructure comprises varying levels of availability, volatility, and trust, allowing volunteers to randomly join and leave the model, which makes the resource management and scheduling of tasks a challenging process. On the other side, it is even more challenging and critical to guarantee the Quality of Service (QoS) for applications deployed in the cuCloud model, which requires the tracking and monitoring the reliability and trust of highly distributed volunteer resources. The majority of the available reputation models consider only the ratio of successfully completed tasks to total tasks requested in the determination of reliability decisions of the volunteer nodes, which, in turn, make the reliability model coarse-grained. These models lack of fine-grained parameters such as task-level behaviors (e.g. success or fail) and task characteristics (e.g. priority of a task). To address these challenges, we propose SaRa, a probabilistic system to estimate the reliability of untrusted edge resources in volunteer cloud. Our validation results showed that SaRa's reputation model obtained better reliability estimation than existing methods.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129624977","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

IEEE EDGE 2018 Organizing Committee IEEE EDGE 2018组委会

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/edge.2018.00006

引用次数: 0

Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection 智能交叉口实时交通模式采集与分析模型

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00028

U. Sreekumar, Revathy Devaraj, Qi Li, Kaikai Liu

{"title":"Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection","authors":"U. Sreekumar, Revathy Devaraj, Qi Li, Kaikai Liu","doi":"10.1109/EDGE.2018.00028","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00028","url":null,"abstract":"The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"59 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930555","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: 10

Enterprise Scale Privacy Aware Occupancy Sensing 企业规模的隐私感知占用传感

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00022

Surya Sajja, Ashok Pon Kumar, Rohun Tripathi, Satyam Dwivedi, Amith Singhee, M. Vermeulen

引用次数: 2

Docker Container Deployment in Fog Computing Infrastructures 雾计算基础设施中的Docker容器部署

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00008

Arif Ahmed, G. Pierre

引用次数: 64

Large Scale Stream Analytics Using a Resource-Constrained Edge 使用资源受限边缘的大规模流分析

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00027

R. Das, G. Bernardo, H. Bal

引用次数: 6