- Data management
- Erasure coding
- MapReduce framework
- Distributed (storage) systems
- Fog and Edge clouds
- Geo-distributed clouds
I’m a member of Avalon and Stack teams of inria research center that are hosted in LIP and LS2N laboratories, respectively. I’m registered at the university (i.e., école) ENS de Lyon for the Ph.D. degree. During my Ph.D., I’m working closely with my two advisors: Shadi Ibrahim and Christian Perez.
I’m working on topics related to Data Management in a single data center and geographically distributed clouds environments.
The first part of my Ph.D. was dedicated for improving service provisioning in geo-distributed clouds and Fog/Edge environments. We worked on network-aware retrieval of Virtual Machine Images in geo-distributed clouds and on the network-aware placement of container images in Edge environment (for more details, our work led to two publications here and here).
Currently, I’m focusing on reducing the storage cost of data intensive clusters. More specifically, we study the opportunities and the challenges in integrating erasure coding for online data analytics. As a first step, through an in-depth performance evaluation study we evaluate how analytic workloads, data persistency, failures, the back-end storage devices, and the network configuration impact the performance for MapReduce jobs running under erasure coding and replication (this work led to a publication here).
Briefly, The subject of this thesis is a scalable and efficient data management for building and running data intensive services.
I’m part of DISCOVERY Initiative, a research project that explores the methodologies and algorithms needed to deploy and operate massively distributed cloud infrastructure as Fog/Edge environments. Developed prototypes and tools are ported to OpenStack, an open source de-facto IaaS manager.
I reviewed papers as subreviewer for ICPP 2019, SmartData 2019, HPBDC 2019, Cluster 2017, CCGRID 2017 and CloudCom 2017.