Proceedings of the Second ACM/IEEE Symposium on Edge Computing最新文献

Enhancing AMBER alert using collaborative edges: poster 利用协作优势增强AMBER警报:海报

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132459

Qingyang Zhang, Quan Zhang, Weisong Shi, Hong Zhong

引用次数: 11

Precog: prefetching for image recognition applications at the edge Precog:边缘图像识别应用程序的预取

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3134456

Utsav Drolia, Katherine Guo, P. Narasimhan

{"title":"Precog: prefetching for image recognition applications at the edge","authors":"Utsav Drolia, Katherine Guo, P. Narasimhan","doi":"10.1145/3132211.3134456","DOIUrl":"https://doi.org/10.1145/3132211.3134456","url":null,"abstract":"Image recognition applications are on the rise. Increasingly, applications on edge devices such as mobile smartphones, drones and cars, are relying on recognition techniques to provide interactive and intelligent functionality. Given the complexity of these techniques, and resource constrained nature of edge devices, applications rely on offloading compute intensive recognition tasks to the cloud. This has also lead to the rise of cloud-based recognition services. This involves sending captured images to remote servers across the Internet, which leads to slower responses. With the rising numbers of edge devices, both, the network and such centralized cloud-based solutions, are likely to be under stress, and lead to further slower responses. To reduce the recognition latency, and provide better scalability to the cloud-based solutions, we propose Precog. Precog employs selective computation on the devices to reduce the need to offload images to the cloud. In coordination with edge servers, it uses prediction to prefetch parts of the trained classifiers used for recognition onto the devices, and uses these smaller models to accelerate recognition on devices. Our evaluation shows that Precog can reduce latency by up to 5×, better utilize edge and cloud resources and also increase accuracy. We believe that Precog is the first system to use devices and edge servers collaboratively to enable prefetching and caching on the devices, and drive down recognition latency for mobile applications.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131785410","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: 60

High speed object tracking using edge computing: poster abstract 使用边缘计算的高速目标跟踪:海报摘要

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132457

J. Dick, Caleb Phillips, S. H. Mortazavi, E. D. Lara

引用次数: 4

Fast and accurate object analysis at the edge for mobile augmented reality: demo 移动增强现实边缘快速准确的对象分析:演示

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132458

Qiang Liu, Siqi Huang, T. Han

引用次数: 7

Edge computing in the ePC: a reality check ePC中的边缘计算:现实检验

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3134449

I. Hadžić, Yoshihisa Abe, Hans C. Woithe

引用次数: 27

Workload management for dynamic mobile device clusters in edge femtoclouds 边缘飞云中动态移动设备集群的工作负载管理

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3134455

Karim Habak, E. Zegura, M. Ammar, Khaled A. Harras

{"title":"Workload management for dynamic mobile device clusters in edge femtoclouds","authors":"Karim Habak, E. Zegura, M. Ammar, Khaled A. Harras","doi":"10.1145/3132211.3134455","DOIUrl":"https://doi.org/10.1145/3132211.3134455","url":null,"abstract":"Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. In our previous work on Femtoclouds, we proposed taking advantage of clusters of devices that tend to be co-located in places such as public transit, classrooms or coffee shops. These clusters can perform computations for jobs generated from within or outside of the cluster. In this paper, we address the full requirements of workload management in Femtoclouds. These functions enable a Femtocloud to provide a service to job initiators that is similar to that provided by a centralized cloud service. We develop a system architecture that relies on the cloud to efficiently control and manage a Femtocloud. Within this architecture, we develop adaptive workload management mechanisms and algorithms to manage resources and effectively mask churn. We implement a prototype of our Femtocloud system on Android devices and utilize it to evaluate the overall system performance. We use simulation to isolate and study the impact of our workload management mechanisms and test the system at scale. Our prototype and simulation results demonstrate the efficiency of the Femtocloud workload management mechanisms especially in situations with potentially high churn. For instance, our mechanisms can reduce the average job completion time by up to 26% compared to similar mechanisms used in traditional cloud computing systems when used in situations that suggest high churn.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131384194","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: 52

Establishing a BLE mesh network with fabricated CSRmesh devices: demo abstract 用自制的CSRmesh设备建立BLE mesh网络:演示摘要

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132460

Xiaokun Yang, Xianlong He

引用次数: 4

A smart building system integrated with an edge computing algorithm and IoT mesh networks: demo abstract 集成边缘计算算法和物联网网状网络的智能建筑系统:演示摘要

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132462

Archit Gajjar, Yunxiang Zhang, Xiaokun Yang

引用次数: 5

Embedded sensor-data compression frameworks for connected lighting systems: poster 连接照明系统的嵌入式传感器数据压缩框架:海报

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132212

Arvind Ramesh, Olaitan Olaleye, A. Murthy

引用次数: 0

CIRCE - a runtime scheduler for DAG-based dispersed computing: demo 基于dag的分布式计算的运行时调度程序:演示

Proceedings of the Second ACM/IEEE Symposium on Edge Computing Pub Date : 2017-10-12 DOI: 10.1145/3132211.3132451

Aleksandra Knezevic, Quynh Nguyen, Jason A. Tran, Pradipta Ghosh, Pranav Sakulkar, B. Krishnamachari, M. Annavaram

引用次数: 10