Detect explosives using ground penetrating radar. Develop autonomous and connected vehicle networks. Create ad-hoc mobile wireless networks. Use LIDAR-equipped terrestrial robots and platforms with sonars and camera sensors, and multicopters with sensing capabilities and on-board computing to research and develop the wireless network and mobile computing applications that change the way we live our lives.
"This research has the potential to not only save and improve lives, but also to advance the basic science of how to combine sensors and information together to get a whole better than the sum of its parts."
Project Title: Improving Reliability of In-Memory Storage
Investigator: Jianhui Yue
Sponsor: National Science Foundation
Overview: Emerging nonvolatile memory (NVM) technologies, such as PCM, STT-RAM, and memristors,
provide not only byte-addressability, low-latency reads and writes comparable to DRAM,
but also persistent writes and potentially large storage capacity like an SSD. These
advantages make NVM likely to be next-generation fast persistent storage for massive
data, referred to as in-memory storage. Yet, NVM-based storage has two challenges:
(1) Memory cells have limited write endurance (i.e., the total number of program/erase
cycles per cell); (2) NVM has to remain in a consistent state in the event of a system
crash or power loss. The goal of this project is to develop an efficient in-memory
storage framework that addresses these two challenges.
This project will take a holistic approach, spanning from low-level architecture design to high-level OS management, to optimize the reliability, performance, and manageability of in-memory storage. The technical approach will involve understanding the implication and impact of the write endurance issue when cutting-edge NVM is adopted into storage systems. The improved understanding will motivate and aid the design of cost-effective methods to improve the life-time of in-memory storage and to achieve efficient and reliable consistence maintenance.
Project Title: Effective Sampling-based Miss Ration Curves: Theory and Practice
Investigator: Zhenlin Wang
Sponsor: National Science Foundation
Overview: Caches, such as distributed in-memory cache for key-value store, often play a key role in overall system performance. Miss ratio curves (MRCs) that relate cache miss ratio to cache size are an effective tool for cache management. This project develops a new cache locality theory that can significantly reduce the time and space overhead of MRC construction and thus makes it suitable for online profiling. The research will influence system design in both software and hardware, as nearly every system involves multiple types of cache. The results can thus benefit a wide range of systems from personal desktops to large scale data centers.
The project investigates a new cache locality theory, applies it to several caching or memory management systems, and examines the impact of different online random sampling techniques. The theory introduces a concept of average eviction time that facilitates modeling data movement in cache. The new model constructs MRCs with data reuse distribution that can be effectively sampled. This model yields a constant space overhead and linear time complexity. The research is focused on theoretical properties and limitations of this model when compared with other recent MRC models. With this lightweight model, the project seeks to guide hardware cache partitioning, improve memory demand prediction and management in a virtualized system, and optimize key-value memory cache allocation.