The coming age of pervasive data processing

The coming age of pervasive data processing

Rellermeyer, Jan S. (TU Delft Data-Intensive Systems)
Omranian Khorasani, S. (TU Delft Data-Intensive Systems)
Graur, Dan (Student TU Delft)
Parthasarathy, Apourva (Student TU Delft)

Iosup, Alexandru (editor)
Pop, Florin (editor)
Prodan, Radu (editor)
Uta, Alexandru (editor)

2019

Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of data-intensive workloads, the ever-increasing demand of applications have made us reconsider the traditional ways of scaling (e.g., scale-out) and seek new opportunities for improving the performance. In order to prepare for an era where data collection and processing occur on a wide range of devices, from powerful HPC machines to small embedded devices, it is crucial to investigate and eliminate the potential sources of inefficiency in the current state of the art platforms. In this paper, we address the current and upcoming challenges of pervasive data processing and present directions for designing the next generation of large-scale data processing systems.

Big Data
Machine Learning
Systems
Performance
Efficiency

http://resolver.tudelft.nl/uuid:fe53f4b2-bdfa-4049-a755-0ce8fd8a6407

https://doi.org/10.1109/ISPDC.2019.00011

IEEE

2020-02-08

978-1-7281-3802-2

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC): Proceedings

18th International Symposium on Parallel and Distributed Computing, ISPDC 2019, 2019-06-05 → 2019-06-07, Amsterdam, Netherlands

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2019 Jan S. Rellermeyer, S. Omranian Khorasani, Dan Graur, Apourva Parthasarathy