Operating Systems and Distributed Systems

Faculty active in the research areas of Operating Systems and Distributed Systems in the Computer Science Department investigate algorithms, design principles, and engineering techniques for developing the software necessary to run modern computer systems. Operating systems research focuses on system software targeting a single machine or physical computational device while distributed systems efforts study the use of multiple computers interconnected by a network to implement coherent, secure, scalable, and reliable systems. Current research foci include cloud computing, distributed database and "Big Data," operating system virtualization, programming languages and runtime environments for distributed systems, machine learning and statistical techniques for large-scale analytics, social networks, and data-center management systems. Researchers in these areas employ collaborative and multi-technological approaches often combining skills and research results from multiple disciplines in a team setting. Together, faculty and students develop solutions to complex problems that lead to a transformative impact on an increasingly information-centric and data-dependent society.

Affilated Labs: 
Distributed Systems, Databases, and Bioinformatics Lab, Distributed Systems Lab, RACELab, SAND Lab

Faculty

Distributed Systems and Databases, Cloud Computing, Big Data, Social Network and Social Media Data Analytics.

He has investigated multi-processor scheduling, systolic arrays, and the relationship between algorithms and architectures for parallel processing.  Via Javelin, CX, and, the JICOS project, he is investigating distributed cloud computing. 

Big Data, Cloud Computing, Social Networks, Fault-tolerant Distributed Systems and Data Management at scale.

Chandra Krintz

Chandra has led a number of research projects that have advanced the state-of-the-art in programming systems in ways that improve performance and energy consumption, and that ease development and deployment of software.

Much of Professor Singh’s research is around data-centric modeling of systems and he focuses on the development of new methods that can be applied to real-world applications.

The goal of my research is to explore ways in which the ubiquitous proliferation of high-performance network connectivity can be used to foster new distributed computing capabilities and systems. 

His recent research is in the fields  of web data mining and search, and cloud systems. His past research includes search engines, scalable web services and middleware, scheduling and runtime support for parallel irregular computation, and parallel sparse matrix algorithms.

Along with Professor H. Zheng, I co-lead the SAND Lab for research on Systems, Algorithms, Networking and Data.  In recent years, my research has taken a data-driven approach to understanding real networking and systems problems, and using data analysis and models to guide the development of solutions using algorithm and systems.