Bochra Boughzala, MSc

PhD Student

About Me

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

Teaching

• 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

Research

• Software-Defined Networking
• Programmable Data Planes
• Distributed Systems
• Data Analytics

Recent publications

1. Window-based Parallel Operator Execution with In-Network Computing: Proceedings (B. Boughzala, B. Koldehofe and C. Gärtner), In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22), ACM New York, NY, USA, 2022.

   Abstract

   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

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   BibTeX

   
   @inproceedings{boughzala2022:win, title = "Window-based Parallel Operator Execution with In-Network Computing: Proceedings", abstract = "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", author = "B. Boughzala and B. Koldehofe and C. G{\"a}rtner", year = "2022", month = may, day = "26", doi = "10.1145/3524860.3539804", language = "English", booktitle = "Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22)", publisher = "ACM New York, NY, USA ", note = " The 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22) ; Conference date: 27-06-2022 Through 30-06-2022", }

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   doi
2. Accelerating the Performance of Data Analytics using Network-centric Processing (B. Boughzala), 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.

   BibTeX

   
   @inproceedings{Boughzala2021:debs, title = "Accelerating the Performance of Data Analytics using Network-centric Processing", author = "B. Boughzala", year = "2021", doi = "https://doi.org/10.1145/3465480.3468162", language = "English", booktitle = "The 15th ACM International Conference on Distributed and Event-based Systems (DEBS '21), June 28-July 2, 2021, Virtual Event, Italy", publisher = "ACM New York, NY, USA ", }

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   doi

(For more publications go to Bochra's publication page)