Dependable Computing Systems Laboratory

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

  1. 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 ]

  2. September 2022: We release a large dataset for time series prediction in collaboration with Adobe Research. This includes an Adobe trace dataset that records CPU and Memory usage for 50 different services running in Adobe production clusters collected for 15 days. Our CIKM paper details this dataset and the associated research advancement. [ Post ]

  3. July 2022: Four DCSL-ers graduate with their PhD, three new DCSL PhD students join. [ Post ]

  4. July 2022: A paper from DCSL from 2012 wins the DSN Test of Time Award. Congratulations to our PhD alums, Amiya (Maji) and Fahad (Arshad), together with our IBM Research collaborator, Jan Rellermeyer. [ WWW ]
  5. March 2022: Two papers, one at CVPR and one at Security and Privacy. The first on scheduling complex vision tasks on mobile GPUs and the second on learning from behavioral economists how to incentivize securing interdependent systems. Congrats to Ran and Mustafa, who led the charge. [ Post ]

  6. December 2021: Two DCSL-ers graduate with their PhDs, Edgardo Barsallo and Ran Xu. We are excited for what great things they will do next. [ 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). DCSL is the co-lead in the WHIN consortium, leading the thrust on “IoT Systems and Networking”.

Last modified: October 23, 2022