We are pleased to announce the RSDA 2020 Keynote by:


Professor in the School of Electrical and Computer Engineering and the Department of Computer Science at Purdue University in West Lafayette, Indiana.

Dependability Meets Data Analytics


We live in a data-driven world as everyone around has been telling us for some time. Everything is generating data, in volumes and at high rates, from the sensors embedded in our physical spaces to the large number of machines in data centers which are being monitored for a wide variety of metrics. The question that we pose is:

Can all this data be used for improving the dependability of computing systems?

Dependability is the property that a computing system continues to provide its functionality despite the introduction of faults, either accidental faults (design defects, environmental effects, etc.) or maliciously introduced faults (security attacks, external or internal). We have been addressing the dependability challenge through large-scale data analytics applied end-to-end from the small (networked embedded systems, mobile and wearable devices) to the large (edge and cloud systems, distributed machine learning clusters). In this talk, I will first give a high-level view of how data analytics has been brought to bear on dependability challenges, and key insights arising from our work. Then I will do a deep dive into the problem of configuring complex systems to meet dependability and performance requirements, using data-driven decisions.

Keynote Speakers in previous RSDA editions:

  • RSDA 2019 - Ingo Weber, TU Berlin: Behavioural Analytics and Blockchain Applications - a Reliability View
  • RSDA 2016 - Hervé Debar, Télécom SudParis: Towards a quantitative approach for threat mitigation and response
  • RSDA 2014 - Leyla Yumer, Symantec Research Labs: From Script-kiddies to Cyberwars...
  • RSDA 2014 - Brendan Murphy, Microsoft Research: The Role of Data Analytics in Reliability and Security Verification
  • RSDA 2013 - Nuno Silva, Critical Software: The Role of Data for Safety Critical Systems Development and Validation