My areas of interest span Operating Systems and Distributed Systems. Within these disciplines, I work mainly in the areas of resource management, scheduling, and performance analysis. I am interested in performance issues in a variety of distributed systems: Clouds, Edge computing, Data centers, and Mobile platforms. The key focus of my work has been to provide system support for data- and compute-intensive applications, and to make these systems scalable, self-managing, sustainable, and reliable.
A complete list of my publications is available here. Please note that this list is generally more up-to-date than the list of projects below.
Some of my current projects include:
Efficient Edge Data Sensing and Analysis: Many emerging applications in social, healthcare, commercial, and public domains rely on vast quantities of data generated via sensors, mobile and IoT devices, located at the edge of the network. Much of this data needs to be analyzed quickly and efficiently to generate useful real-time insights and actions for users. In this project, we are developing software frameworks and techniques to enable efficient edge data collection, sensing, inference, and analytics. Our goal is to provide high accuracy results while meeting application time constraints in the presence of limited and heterogeneous edge resources. In addition, we want to achieve high resource efficiency and sustainability.
Geo-Distributed Data Analytics: Across a large number of application domains that include web analytics, social analytics, scientific computing, and energy analytics, large quantities of data are generated from disparate sources such as users, devices, and sensors located around the globe. Much of this data needs to processed and analyzed quickly to extract timely information, leading to tradeoffs in cost, performance, and accuracy. In this project, we are developing new scheduling algorithms and resource management techniques for optimizing data-intensive analytics in geo-distributed environment, ranging from stream computing to database queries to machine learning, including training and inference.
Some earlier projects that I have worked on: