Software ecosystems bring value by integrating projects related to a given domain, for example, open source projects in a Linux distribution or mobile apps on the Android platform. However, the major challenge of managing an infrastructure ecosystem like OpenStack or Debian is to provide a polished, well-integrated product to the end user, since each individual project has its own release cycle and roadmap. To understand how modern ecosystems deal with this challenge, I empirically study the release synchronization strategy of the OpenStack ecosystem, in which a central release management team manages the six-month release cycle of the overall OpenStack product. By studying one year of release team IRC meeting logs, 9 major federated release management activities were identified, which were cataloged and documented. My findings suggest that even though an ecosystem’s power lies in the interaction of autonomous projects, release synchronization is a non-trivial goal. Currently, I am performing interviews with key software developers within the OpenStack ecosystem, in order to understand the major activities, which they discuss throughout the release cycle.

Ph.D. Cand., Software Engineering, MCIS – Laboratory, École Polytechnique de Montréal

OpenStack Member Level: Foundation Member

• I am focused on empirical research on ecosystems software releases. I apply mixed method research as a means to answer both the how and why in my findings, which is beneficiary to both the academic and industrial communities. I am Using a DevOps approach with machine learning to make pivotal decisions. Currently, I am involved with the release team of OpenStack and serving as the PC chair of AI and HPC track, for the upcoming submit at Denver 2019. Additionally, I am a Big Data analytics guy.

• Before my current experience, I had Worked on interesting software engineering topics for swarms of robots. Moreover, my expertise in cloud computing is an asset, which I bring along. In my thesis, I investigated the phenomena of live migrations and how we can apply both the supervised and reinforced learning technique (Machine learning algorithms) on live migration in the cloud.

• I was part of the team; team lead on an intriguing project at LASSENA Laboratory Montreal, Canada. My main task includes building test bench simulator for micro iBB black boxes for cars in other to reconstruct accident scenario to understand driver’s behavior.

• Currently, I am serving as a Science Judge for Quebec and Canada (Canada-Wide-Science)

• Research Associate at MCIS Laboratory École Polytechnique de Montréal,

Member: - ACM - IEEE.