Associate Professor, IRISA / INSA Rennes
IRISA / Inria Rennes – Bretagne Atlantique
Campus Universitaire de Beaulieu
35042, Rennes, France
Phone: + 33 2 99 84 75 66
E-mail: alexandru.costan at irisa.fr
My main research interests are: Big Data management on large scale infrastructures like clouds, fog, edge, stream processing and workflow data management.
- [HDR Defense] I defended my HDR titled: “From Big Data to Fast Data: Efficient Stream Data Management” on March 14, 2019, at ENS Rennes
- [Principal Investigator] of the SmartFastData Associate Team with the Instituto Politécnico Nacional, Mexico, budget 20,000 EUR
- [Co-chair] I am serving as co-chair of the ScienceCloud’19 workshop, organised with ACM HPDC 2019. Your submissions are welcome!
- [Programme Committees] 2019: I am serving as PC member for IEEE/ACM SuperComputing 2019 (Clouds and Distributed Computing Track, Posters Track, ACM Student Research Competition), IEEE/ACM CCGrid 2019 (Cloud Computing Track), IEEE BigData 2019
- [Community] Member of the Best Poster Award Committee for IEEE/ACM SuperComputing 2018
- [Journal Articles] Our papers “Keeping up with storage: Decentralized, write-enabled dynamic geo-replication” and “Mission Possible: Unify HPC and Big Data Stacks Towards Application-Defined Blobs at the Storage Layer” were accepted for publication in Future Generation of Computer Systems
- [PhD Defense] 2018: congratulations to Ovidiu Marcu for defending his thesis: “KerA: A Unified Ingestion and Storage System for Scalable Big Data Processing”!
- [Workshop Paper] Our paper “Planner: Cost-efficient Execution Plans Placement for Uniform Stream Analytics on Edge and Cloud” was accepted at the WORKS 2018 workshop, held in conjunction with IEEE/ACM SuperComputing 2018
- [Conference Paper] Our paper “TýrFS: Increasing Small Files Access Performance with Dynamic Metadata Replication” was accepted at IEEE/ACM CCGrid 2018
- [PhD Defense] 2018: congratulations to Pierre Matri for defending his thesis: “Tyr: Storage-Based HPC and Big Data Convergence Using Transactional Blobs”!
- [Journal Article] Our paper “Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud” was accepted for publication in IEEE Transactions on Knowledge and Data Engineering
- [Conference Papers] Our short papers “KerA: Scalable Data Ingestion for Stream Processing” and “SLoG: Large-Scale Logging Middleware for HPC and Big Data Convergence” were accepted at IEEE ICDCS 2019
- [Co-chair] I am serving as co-chair of the ScienceCloud’18 workshop, organised with ACM HPDC 2018
- [PhD Defense] 2017: congratulations to Luis Pineda for defending his thesis: “Efficient support for data-intensive scientific workflows on geo-distributed clouds”!
- [Principal Investigator] of the ANR OverFlow project, budget 250,000 EUR
- [Co-chair] I am serving as co-chair of the ScienceCloud’17 workshop, organised with ACM HPDC 2017
- [Conference Paper] Our paper “Tyr: Blob Storage Meets Built-In Transactions” was accepted for publication at ACM/IEEE SuperComputing’16 (Acceptance Rate: 17%), Best Student Paper Finalist
- [Editor] of the Soft Computing journal Special Issue on: Autonomic Computing and Big Data Platforms
Alexandru Costan is an Associate Professor at INSA Rennes and a researcher within the KerData team at IRISA Rennes. In 2011, he obtained a Ph.D. in Computer Science from the Politehnica University of Bucharest (PUB), under the supervision of Valentin Cristea (PUB) and Iosif Legrand (California Institute of Technology). The Ph.D. thesis focused on self-adaptive behavior of large-scale distributed systems based on monitoring information, bringing several contributions to the MonALISA monitoring system, developed in collaboration with Caltech and CERN. After the Ph.D., Alexandru joined Inria as a postdoctoral researcher within the KerData team, working with Gabriel Antoniu and Luc Bougé, on scalable storage in cloud environments. In 2012, he became an Associate Professor at INSA Rennes, where he is currently leading the Big Data Science track. His research interests include Big Data management in HPC and clouds, fast data and stream processing, autonomic behavior and workflow management. At IRISA, he is currently involved in several research projects working on efficient Big and Fast Data management on clouds and edge infrastructures in the context of several collaborations with Microsoft Research and Huawei Research. Alexandru has published one book, more than 20 articles in international journals and 30 papers in international conferences. He serves as PC member of several top-level conferences and workshops in the domain of distributed computing (SuperComputing, CCGrid, Cluster, Big Data). Since 2011 he is the co-chair of the BigDataCloud workshop at EuroPar as well as the ScienceCloud workshop at HPDC (since 2015). He is currently leading the ANR OverFlow project and and he is a member of the JLESC: Joint Laboratory on Extreme-Scale Computing.
A list of my recent publications can be found on HAL Open Archives Library and DBLP:
- Planner is middleware for cost-efficient execution plans placement for uniform stream analytics on Edge and Cloud. Planner automatically selects which parts of the execution graph will be executed at the Edge in order to minimize the network cost. Real-world micro-benchmarks show that Planner reduces the network usage by 40% and the makespan (end-to-end processing time) by 15% compared to state-of-the-art.
- KerA is a low-latency storage for stream processing (currently under development at Inria, in collaboration with Universidad Politécnica de Madrid, in the framework of a contractual partnership between Inria and Huawei Munich). By eliminating storage redundancies between data ingestion and storage, preliminary experiments with KerA successfully demonstrated its capability to increase throughput for stream processing.
- Tyr is a transactional object storage system aimed at storage-based convergence between HPC and Big Data. Tyr natively offers data access coordination in the form of transactions. It offers a POSIX-compliant, transactional, distributed file system implementation built as a thin layer atop. This file system performs well and shows near-linear scalability properties on both HPC and Big Data platforms.
- JetStream is a high performance batch-based streaming middleware. The system is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites.
- TomusBlobs is a concurrency-optimized data storage system which federates the virtual disks associated to the Virtual Machines running the application code on the cloud. It is used in the Azure cloud for efficient data-intensive processing based on MapReduce.
- MonALISA, which stands for Monitoring Agents using a Large Integrated Services Architecture, has been developed over the last years by Caltech and its partners with the support of the U.S. CMS software and computing program. The framework is based on Dynamic Distributed Service Architecture and is able to provide complete monitoring, control and global optimization services for complex systems.
- ANR OverFlow (2015-2019): I am the Principal Investigator of this ANR JCJC project focusing on Workflow Data Management as a Service for Multi-site Applications. Budget: 250,000 Euros.
- BigStorage (2015-2018) is an European Training Network (ETN) project. Area: Storage-based Convergence of HPC and Cloud infrastructures to handle Big Data. Role: local coordinator for Inria Rennes Bretagne Atlantique.
- Z-CloudFlow (2013-2016): geographically distributed workflows on Azure clouds. A project of the Microsoft-Inria Joint Research Centre.
- Pedro Silva (2018-2019). Research topic: “Stream processing on Cloud, Edge and hybrid Cloud/Edge environments”
- Paul Le Noac’h (2016-2019) co-advised with Luc Bougé (ENS Rennes / IRISA). Topic: “Workflow Data Management as a Service for Multi-Site Applications”
Former PhD students
- Ovidiu Marcu (2015-2018), co-advised with Gabriel Antoniu (Inria) and Maria S. Péréz (Universidad Politécnica de Madrid). Topic: “KerA: A Unified Ingestion and Storage System for Scalable Big Data Processing” . Currently, Research Engineer at Inria Rennes, France
- Pierre Matri (2015-2018), co-advised with Gabriel Antoniu (Inria) and Maria S. Péréz (Universidad Politécnica de Madrid). Topic: “Tyr: Storage-Based HPC and Big Data Convergence Using Transactional Blob”. Currently, Postdoctoral Researcher at Argonne National Laboratory, USA
- Luis Pineda (2014-2017), co-advised with Gabriel Antoniu (Inria). Topic: “Efficient support for data-intensive scientific workflows on geo-distributed clouds”. Currently, Research Engineer at Activeeon
- Radu Tudoran (2011-2014), co-advised with Gabriel Antoniu (Inria) and Luc Bougé (ENS Rennes / IRISA). Topic: “Efficient Big Data Management across Cloud Data Centers”. Currently, Principal Research Engineer at Huawei European Research Center, Germany
- INSA Rennes: Big Data Algorithms – lectures and practical sessions (36h / year since 2015)
- INSA Rennes: Big Data Storage and Processing – M1 – lectures and practical sessions (36h / year since 2014)
- INSA Rennes: Introduction to Databases – L2 – lectures and practical sessions (68h / year since 2012)
- ENS Rennes: Introduction to Java Programming – L3 – lectures (17h / year since 2011)
- From 2006 to 2010, I gave several Master and Bachelor lectures and practical sessions on Distributed Systems, Communication Protocols and Distributed Algorithms at the Computer Science Department of University Politehnica of Bucharest