An Empirical Study of Latency in an Emerging Class of Edge Computing Applications for Wearable Cognitive Assistance

被引：113

作者：

Chen, Zhuo ^{[1
]}

Hu, Wenlu ^{[1
]}

Wang, Junjue ^{[1
]}

Zhao, Siyan ^{[1
]}

Amos, Brandon ^{[1
]}

Wu, Guanhang ^{[1
]}

Ha, Kiryong ^{[1
]}

Elgazzar, Khalid ^{[1
]}

Pillai, Padmanabhan ^{[2
]}

Klatzky, Roberta ^{[1
]}

Siewiorek, Daniel ^{[1
]}

Satyanarayanan, Mahadev ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

[2] Intel Labs, Santa Clara, CA USA

来源：

SEC 2017: 2017 THE SECOND ACM/IEEE SYMPOSIUM ON EDGE COMPUTING (SEC'17) | 2017年

基金：

美国国家科学基金会;

关键词：

Cloudlet; Edge Computing; Mobile Computing; Cloud Computing; Smart Glass; Augmented Reality; HoloLens; RECOGNITION; SYSTEM;

D O I：

10.1145/3132211.3134458

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

An emerging class of interactive wearable cognitive assistance applications is poised to become one of the key demonstrators of edge computing infrastructure. In this paper, we design seven such applications and evaluate their performance in terms of latency across a range of edge computing configurations, mobile hardware, and wireless networks, including 4G LTE. We also devise a novel multi-algorithm approach that leverages temporal locality to reduce end-to-end latency by 60% to 70%, without sacrificing accuracy. Finally, we derive target latencies for our applications, and show that edge computing is crucial to meeting these targets.

引用

页数：14

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