Computer Science Thesis Oral
- Newell-Simon Hall
- 3305 (Remote Access Enabled)
- JUNJUE WANG
- Ph.D. Student
- Computer Science Department
- Carnegie Mellon University
Scaling Wearable Cognitive Assistance
While previous research has demonstrated the technical feasibility of wearable cognitive assistants, many practical concerns have not been addressed. First, previous work operates the wireless networks and cloudlets at low utilization in order to meet application latency. The economics of practical deployment precludes operation at such low utilization. Second, previous work on the Gabriel framework does not address the most time-consuming parts of creating a wearable cognitive assistance application. Experience has shown that developing computer vision modules that analyze video feeds is a time-consuming and painstaking process. Development tools that alleviate the time and the expertise needed can greatly facilitate the creation of these applications.
In this dissertation, we address the problem of scaling wearable cognitive assistance. Scalability here has a two-fold meaning. First, a scalable system supports a large number of associated clients with a fixed amount of infrastructure and is able to serve more clients as resources increase. Second, we want to enable a small software team to quickly create, deploy, and manage these applications. We claim that: Two critical challenges to the widespread adoption of wearable cognitive assistance are 1) the need to operate cloudlets and wireless networks at low utilization to achieve acceptable end-to-end latency 2) the level of specialized skills and the long development time needed to create new applications. These challenges can be effectively addressed through system optimizations, functional extensions, and the addition of new software development tools to the Gabriel platform.
We validate this thesis in this dissertation. The main contributions of the dissertation are as follows: 1. We propose application-agnostic and application-aware techniques to reduce bandwidth consumption and offered load when the cloudlet is oversubscribed. 2. We provide a profiling-based cloudlet resource allocation mechanism that takes account of diverse application adaptation characteristics. 3. We propose a new prototyping methodology and create a suite of development tools to reduce the time and lower the barrier of entry for WCA creation.
Mahadev Satyanarayanan (Chair)
Padmanabhan Pillai (Intel Labs)
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