Bochra Boughzala, MSc
I joined the Distributed Systems research group at the University of Groningen in Feb. 2021, where I am currently a PhD student and teaching assistant at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence. My research interests evolved around computer networks, software-defined networking, packet processing for switching and routing, programmable data planes, P4 programming language and more recently distributed systems, middleware and complex event processing.
Prior to joining University of Groningen, I was a Software Designer at Kaloom Inc, Montreal and before that an Experienced Researcher at Ericsson Research Montreal, Canada. I received my M.Sc. (2013) in Computer Science from UQAM (University of Quebec in Montreal). Before that, I completed my Engineering degree in Computer Networks (2010) at the National Institute of Applied Sciences and Technology (INSAT), Tunis, Tunisia.
- room number: 596
- e-mail: b.boughzala [at] rug.nl
- profile page: B. Boughzala
- WBCS026-05 (2022-2023) : Research Skills in Computing Science
- WMCS022-05 (2022-2023) : Fundamentals of Distributed Systems
- WBCS031-05 (2022-2023) : Network Centric Systems
- WBCS031-05 (2021-2022) : Network Centric Systems
- Software-Defined Networking
- Programmable Data Planes
- Distributed Systems
- Data Analytics
- Window-based Parallel Operator Execution with In-Network Computing: Proceedings ( ), In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22), ACM New York, NY, USA, 2022.
Data parallel processing is a key concept to increase the scalability and elasticity in event streaming systems. Often data parallelism is accomplished in a splitter-merger architecture where the splitter divides incoming streams into partitions and forwards them to parallel operator instances. The splitter performance is a limiting factor to the system throughput and the parallelization degree.This work studies how to leverage novel methods of in-network computing to accelerate the splitter functionality by implementing it as an in-network function. While dedicated hardware for in-network computing has a high potential to enhance the splitter performance, in-network programming models like the P4 language are also highly limited in their expressiveness to support corresponding parallelization models. We propose P4SS which supports overlapping and non-overlapping count-based windows for multiple independent data streams and parallelizes them to a dynamically configurable number of operator instances. We validate in the context of a prototypical implementation our splitting strategy and its scalability in terms of switch resource consumption.
Keywords: Data Parallelism, In-network Computing, Load Balancing, Complex Event Processing (CEP), P4 Language, Data Plane Programming
- Accelerating the Performance of Data Analytics using Network-centric Processing ( ), In The 15th ACM International Conference on Distributed and Event-based Systems (DEBS '21), June 28-July 2, 2021, Virtual Event, Italy, ACM New York, NY, USA, 2021.
(For more publications go to Bochra's publication page)