Short Bio & Research Interest
I am a systems researcher (Senior Inventive Scientist)
in Cloud Platform Software Research group at AT&T Labs Research.
I am broadly interested in solving challenging problems related to mobile and cloud systems.
I earned a Ph.D. degree in the Computer Science Department at
University of Southern California (USC) in 2013.
I have worked on various topics in this exciting problem space. Recent work in AT&T Labs Research includes software-defined storage with customizable quality of service [SDS, IOArbiter] and privacy-preserving systems for wearable devices [DNC]. Before joining AT&T Labs, I built several interesting cloud-enabled mobile systems, including a privacy-preserving photo sharing systems [P3, NSDI'13] that has been also commercialized, a high-level programming systems for crowd-sensing tasks [Medusa, MobiSys'12], a runtime that enables demanding mobile applications by offloading computation to the cloud [Odessa, MobiSys'11], and a smart network interface selection system and associated algorithm that can tradeoff energy and delay by intelligently deferring transmission opportunities [SALSA, MobiSys'10].
Yi-Hsuan Kao, Bhaskar Krishnamachari, Moo-Ryong Ra, and Fan Bai
- "Hermes: Latency Optimal Task Assignment for Resource-constrained Mobile Computing",
- IEEE INFOCOM 2015, [paper]
Bo Han, Feng Qian, and Moo-Ryong Ra (equal contribution)
- "Human-Assisted Positioning Using Textual Signs",
- ACM HotMobile 2015, [paper]
- "Mobile Videos: Where Are We Headed?",
- IEEE Internet Computing, January/February 2015 (invited article)
- "Cloud-Enabled Mobile Sensing Systems",
- Ph.D. Thesis, June 2013, [paper]
- Moo-Ryong Ra, Ramesh Govindan, and Antonio Ortega
Moo-Ryong Ra, Bodhi Priyantha, Aman Kansal, and Jie Liu
- "Improving Energy Efficiency of Personal Sensing Applications with Heterogeneous Multi-Processors",
- ACM Ubicomp 2012, [paper]
- Moo-Ryong Ra, Bin Liu, Tom La Porta, and Ramesh Govindan
- Moo-Ryong Ra, Anmol Sheth, Lily M. Mummert, Padmanabhan Pillai, David Wetherall, and Ramesh Govindan
- Moo-Ryong Ra, Jeongyeup Paek, Abhishek B. Sharma, Ramesh Govindan, Martin H. Krieger, and Michael J. Neely
[SDS] Software-Defined Storage with Customizable QoS
Software-Defined Storage (SDS) is a critical component in the realization of virtualized data centers. This project envisions AT&T’s concept of SDS where a highly individualized storage solution can by dynamically instantiated on an underlying Cloud QoS platform, which provides virtualized compute, storage, and networking resources with support for QoS. We are designing robust and scalable system architecture and building prototype systems in this space.
[IOArbiter] Guaranteeing SLAs for Storage Traffic in Virtualized Data Centers
In today’s data centers, storage hardwares are getting virtualized and shared across multiple tenants. However, in order to properly materialize expected benefits, performance isolation and guaranteed performance for I/O traffic are important since demanding tenants can severely interfere the application’s I/O performance. In this project, we explore the necessity of simultaneous arbitration of multiple system resources and aim to design an orchestration system that can protect VMs’ I/O traffic from noisy neighbors.
[DNC] Privacy Protection from Prying Eyes for Wearable Devices
Smartphones and tablets are excellent point-and-shoot cameras, with users taking dozens of pictures and videos anytime and everywhere. Wearables like smart goggles can record media in both public and private places, with little or no awareness from the subjects in the surroundings. The pervasive use of these devices can compromise the privacy of all the individuals that are unaware subjects of these pictures and videos, which could also be published without explicit consent on the Internet and social media. This project proposes a novel technology that removes unwilling subjects from media that includes them at capture time.
[P3] Privacy-Preserving Photo Sharing
Photo sharing services, e.g. Facebook and Flickr, are getting more popular nowadays, however, there are no protection of users' photos against the photo sharing service providers (PSPs). We are just forced to trust PSPs. In this work, we develop a photo encryption algorithm that can preserve privacy against PSPs while still maintaining the useful processing services provided by them, e.g. image scalability and quality enhancement. We also built a prototype transparently worked with Facebook. (NSDI 2013, app)
[Medusa] Programming Crowd-Sensing Task
Design and implementation of a high level programming language and associated runtime for crowd-sensing applications. The framework makes two strands of topics extremely easy to program, a) tasking smartphones and b) dealing with human mediation and incentives. (MobiSys 2012, code, demo video)
[MultiSensing] Multi-Processor Sensing
Attempt to answer the following question: In order to enable continuous personal sensing applications on mobile devices, how should we use ultra low power processor(LP) with an existing application processor(AP)? Investigate representative app components and explore the placement strategy between AP and LP that brings minimum energy consumption. (with MSR Redmond SERG group, Ubicomp'12)
[Odessa] Enabling Perception Applications on Mobile Devices
Partitioning mobile perception applications across mobile devices and the cloud infrastructure focusing on the performance. Design and implementation of lightweight, i.e. sub-second decision granularity, dynamic decision engine and associated runtime that can adapt to input variability, network and device heterogeneity in real-time. (with Intel Labs Seattle, MobiSys'11)
[SALSA] Energy-Efficient Network Interface Selection
Design and implementation of SALSA (Stable and Adaptive Link Selection Algorithm) for high data-rate and delay-tolerant mobile applications. Our algorithm intelligently defers the transmission opportunity to the future based on Lyapunov optimization framework anticipating that more energy-efficient WiFi AP, rather than 3G, would be avaiable soon. (MobiSys'10)
[UrbanTomography] Automatic Video Uploading System
We built an Urban Tomography system, which collects high resolution videos using smartphones and automatically uploads them to an Internet-connected server without any user interventiton. When transfer, the system deals with an automatic AP management issue, e.g. chunking data, network profiling for proper AP selection, blacklisting, etc. It had deployed to the LAX international airport for more than a year and the course projects in USC and UCLA. (JUT'10, JPER'09, [poster])
|Initially created: 8th Oct. 2007 , Last Modified: 24th Nov. 2014|