KERSTREAM

KERSTREAM is a JCJC project funded by the French National Research Agency (ANR). This project aims to address the limitations of Hadoop when running stream Big Data applications on large-scale clouds and to do a step beyond Hadoop by proposing a new approach, called KERSTREAM, for scalable and resilient stream Big Data processing on clouds.
Duration: 60 months (2017-2022)
Budget: 238,000 EUR
Coordinator: Shadi Ibrahim (Inria Research Scientist)

Open Positions

I am looking for highly motivated Master students and Post-docs to work on scalable and dependable Stream Big data processsing for Spring and Fall 2021. Please feel free to email me your resume.

• [Post-Doc position] Topic on optimizing stream data application in the clouds. Funded by the ANR Kerstream project [More Details]. Seeking for those with strong systems experience.
• [Internsnips] Topics on Data management in distributed environments. Funded by the ANR KerStream project [More Details].

Recent Highlight

• [Paper] July 2020: Our paper on Rethinking Operators Placement of Stream Data Application in the Edge in 29th ACM International Conference on Information and Knowledge Management (CIKM 2020). (Short Paper)
• [Press: Interview] Oct 2019: Inria emergences, Un intergiciel post-Hadoop pour gérer les flux de données.
• [Paper] Oct 2019: Our paper on Cost-Aware Partitioning for Efficient Large Graph Processing in Geo-Distributed Datacenters is accepted in IEEE Transactions on Parallel and Distributed Systems.
• [Paper] Sep 2019: Our paper on Exploiting the Power of Choice for Efficient Shuffling in MapReduce is accepted in IEEE Transactions on Big Data.
• [Paper] July 2019: Our paper on revisiting Erasure Coding in Data-intensive clusters is accepted in IEEE Mascots 2019.
• [Paper] May 2019: Our paper on Incorporating Probabilistic Optimizations for Resource Provisioning of Data Processing Workflows is accepted in ICPP 2019.
• [Paper] May 2019: Our paper on the Importance of container images placement for service provisioning in the Edge Low-Latency Data Stream Processing is accepted in ICCCN 2019.
• [Paper] April 2019: Our paper on When FPGA-accelerator meets Stream Data Processing in the Edge is accepted in ICDCS 2019.
• [Paper] April 2019: Our paper on Evaluating Straggler Detection Mechanisms in MapReduce is accepted in ACM Transactions on Modeling and Performance Evaluation of Computing Systems.
• [Paper] Jan 2019: Our paper on NCQ-Aware I/O Scheduling for Conventional Solid State Drives is accepted in IPDPS 2019.
• [Talk] 2018: I presented our joint work on stream data processing "Dual-Paradigm Stream Processing" at ICPP 2018, Eugene, Oregon, USA..
• [Paper] 2018: Our paper on Energy-Efficient Speculative Execution using Advanced Reservation is accepted in ICPP 2018
• .[Paper] 2018: Our paper on Dual-Paradigm Stream Processing is accepted in ICPP 2018.
• [Paper] 2018: Our paper on Low-Latency Data Stream Processing is accepted in ICDCS 2018.
• [Paper] 2018: Our paper on Network-Aware Virtual Machine Image Management in Geo-Distributed Clouds is accepted in CCGrid 2018.
• [Paper] 2018: Our paper on the Performance of Spark on HPC Systems is accepted in SupercomputingAsia 2018 SCA18. Best Paper Candidate.
• [Paper] 2018: Our paper on Improving the effectiveness of burst buffers for big data processing in HPC systems is accepted in FGCS Journal 2018
• [Invited talk] 2018: I gave a talk on scalable Big Data Management on clouds and HPC systems. Invited talk at Huazhong University of Science and Technology, China.
• [Panel] 2017: panelist in the 'Big Data and Analytics' panel at NSS-ICA3PP-CIT 2017.
• [Invited talk] 2017: I gave a talk on Big Data Processing in the Cloud: Hadoop and Beyond at RESCOM 2017 Summer school.

  [Paper] 2017: Our paper on Energy-Driven Straggler Mitigation in MapReduce is accepted in Euro-par 2017.
• [Paper] 2017: Our paper on the Effectiveness of Burst Buffers for Big Data Processing in HPC systems is accepted in Cluster 2017 (Short paper).
• [Paper] 2017: Our paper on Efficient Data Transfer for Graph Processing in Geo-Distributed Datacenters is accepted in ICDCS 2017 (Applications and Experiences Track).
• [Paper] 2017: Our Paper on Characterizing Performance and Energy-efficiency of The RAMCloud Storage System is accepted in ICDCS 2017 (Applications and Experiences Track).