Wenlu Hu

Towards wearable cognitive assistance

We describe the architecture and prototype implementation of an assistive system based on Google Glass devices for users in cognitive decline. It combines the first-person image capture and sensing capabilities of Glass with remote processing to perform real-time scene interpretation. The system architecture is multi-tiered. It offers tight end-to-end latency bounds on compute-intensive operations, while addressing concerns such as limited battery capacity and limited processing capability of wearable devices. The system gracefully degrades services in the face of network failures and unavailability of distant architectural tiers.

Edge Analytics in the Internet of Things

High-data-rate sensors, such as video cameras, are becoming ubiquitous in the Internet of Things. This article describes GigaSight, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The GigaSight architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet, thus reducing the demand for ingress bandwidth into the cloud. Denaturing, which is an owner-specific reduction in fidelity of video content to preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics. This article is part of a special issue on smart spaces.

Where do developers log? an empirical study on logging practices in industry

System logs are widely used in various tasks of software system management. It is crucial to avoid logging too little or too much. To achieve so, developers need to make informed decisions on where to log and what to log in their logging practices during development. However, there exists no work on studying such logging practices in industry or helping developers make informed decisions. To fill this significant gap, in this paper, we systematically study the logging practices of developers in industry, with focus on where developers log. We obtain six valuable findings by conducting source code analysis on two large industrial systems (2.5M and 10.4M LOC, respectively) at Microsoft. We further validate these findings via a questionnaire survey with 54 experienced developers in Microsoft. In addition, our study demonstrates the high accuracy of up to 90% F-Score in predicting where to log.

Quantifying the Impact of Edge Computing on Mobile Applications

Computational offloading services at the edge of the Internet for mobile devices are becoming a reality. Using a wide range of mobile applications, we explore how such infrastructure improves latency and energy consumption relative to the cloud. We present experimental results from WiFi and 4G LTE networks that confirm substantial wins from edge computing for highly interactive mobile applications.

The Case for Offload Shaping

When offloading computation from a mobile device, we show that it can pay to perform additional on-device work in order to reduce the offloading workload. We call this offload shaping, and demonstrate its application at many different levels of abstraction using a variety of techniques. We show that offload shaping can produce significant reduction in resource demand, with little loss of application-level fidelity.

Early Implementation Experience with Wearable Cognitive Assistance Applications

A cognitive assistance application combines a wearable device such as Google Glass with cloudlet processing to provide step-by-step guidance on a complex task. In this paper, we focus on user assistance for narrow and well-defined tasks that require specialized knowledge and/or skills. We describe proof-of-concept implementations for four different tasks: assembling 2D Lego models, freehand sketching, playing ping-pong, and recommending context-relevant YouTube tutorials. We then reflect on the difficulties we faced in building these applications, and suggest future research that could simplify the creation of similar applications.

QuiltView: a crowd-sourced video response system

Effortless one-touch capture of video is a unique capability of wearable devices such as Google Glass. We use this capability to create a new type of crowd-sourced system in which users receive queries relevant to their current location and opt-in preferences. In response, they can send back live video snippets of their surroundings. A system of result caching, geolocation and query similarity detection shields users from being overwhelmed by a flood of queries.

You can teach elephants to dance: agile VM handoff for edge computing

VM handoff enables rapid and transparent placement changes to executing code in edge computing use cases where the safety and management attributes of VM encapsulation are important. This versatile primitive offers the functionality of classic live migration but is highly optimized for the edge. Over WAN bandwidths ranging from 5 to 25 Mbps, VM handoff migrates a running 8 GB VM in about a minute, with a downtime of a few tens of seconds. By dynamically adapting to varying network bandwidth and processing load, VM handoff is more than an order of magnitude faster than live migration at those bandwidths.

An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance

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.

Live Synthesis of Vehicle-Sourced Data Over 4G LTE

Accurate, up-to-date maps of transient traffic and hazards are invaluable to drivers, city managers, and the emerging class of self-driving vehicles. We present LiveMap, a scalable, automated system for acquiring, curating, and disseminating detailed, continually-updated road conditions in a region. LiveMap leverages in-vehicle cameras, sensors, and processors to crowd-source hazard detection without human intervention. We build a real-time simulation framework that allows a mix of real and simulated components to be tested together at scale. We demonstrate that LiveMap can work well at city scales within the limits of todays cellular network bandwidth. We also show the feasibility of accurate, in-vehicle, computer-vision-based hazard detection.