The Dependable Computing Systems Laboratory (DCSL) at Purdue University investigates the question of how to build dependable, heterogeneous, large-scale distributed systems.
“Dependability meets Data Analytics, and at Large Scales”
The above sums up our current research direction. We work on software systems to enable them to perform their functionality in the face of natural and malicious failures. We apply and adapt data analytic techniques to work with the noise of computer systems and at large system scales. Current application domains come from distributed software systems, embedded systems, cellular systems, and bioinformatics.
Latest News
- May 2024: DCSL Award Winners for Spring announced. [ WWW ]
- April 2024: Saurabh presented the NSF/USDA project “Secure distributed protocols for Cyber Physical Systems” at the NSF CPS PI meeting in Vanderbilt on November 20-21. [ WWW ]
- Saurabh was also a panelist in the “Robust and Secure Learning in IoT” panel. [ WWW ]
- Saurabh was also a panelist in the “Robust and Secure Learning in IoT” panel. [ WWW ]
- November 2023: Two DCSL students win prizes for posters at the CRISP NSF Grand Challenges in Resilience Workshop. Pengcheng Wang for his work on 3D object detection on embedded GPUs and Akhil Sai for his work on drone swarms for surveillance. [ WWW ]
- August 2023: Three new graduate students join DCSL — Kasra Derakhshandeh, Sheetal Prasanna, and Muhammad Taha. [ Post ]
- February 2023: Financial Cryptography, Middleware, and CVPR — oh my, what a unique and distinguished combination of conferences where our papers got accepted. Kudos to Akhil, Josh Z, and Atul. [ Post ]
- January 2023:
- October 2022: Paper to appear in NeurIPS, on root cause analysis of microservice-based cloud applications, led by Azam and in collaboration with Adobe Research. [ Post ]
Research Theme at DCSL
Since many business and life critical functions are being performed by distributed systems, they need to be dependable while meeting their performance goals. Thus, there is need for smart error detection, diagnosis, and recovery protocols. Since many of these systems operate on vast amounts of data and the patterns of errors or normal operation are approximate and noisy, we have to adapt leading-edge machine learning tools to these systems problems. There is also need for architectures that can combine dependability and security aspects without significantly degrading performance and do this in an adaptive manner, adapting to different user requirements and different runtime environments. This is our mission at DCSL.
Our application contexts come from various domains, many from our industrial colleagues. These include: security-critical enterprise (with Missile Defense Agency, Northrop Grumman and Lockheed Martin), mobile and cloud platforms (in collaboration with AT&T and IBM), large-scale scientific clusters and applications (in collaboration with Lawrence Livermore National Lab and Argonne National Lab), and cyber physical systems (in collaboration with GE Global Research Center and Sandia).
DCSL is the founding lab within the Purdue College of Engineering Center for Resilient Infrastructures, Systems, and Processes (CRISP), the Army’s Assured Autonomy Innovation Institute (A2I2), and the Purdue University-wide Institute for Physical AI (IPAI).