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Constant-time sliding window framework with reduced memory footprint and efficient bulk evictions

Author
Villalba, Á.; Berral, J.; Carrera, D.
Type of activity
Journal article
Journal
IEEE transactions on parallel and distributed systems
Date of publication
2019-03
Volume
30
Number
3
First page
486
Last page
500
DOI
https://doi.org/10.1109/TPDS.2018.2868960 Open in new window
Project funding
Computación de Altas Prestaciones VII
HiEST: Holistic Integration of Emerging Supercomputing Technologies
Models de Programacio i Entorns d'eXecució PARal.lels
Repository
http://hdl.handle.net/2117/121867 Open in new window
URL
https://ieeexplore.ieee.org/document/8456588 Open in new window
Abstract
The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large data centers, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Stream processing platforms are required to be malleable and absorb spikes genera...
Citation
Villalba, Á., Berral, J., Carrera, D. Constant-time sliding window framework with reduced memory footprint and efficient bulk evictions. "IEEE transactions on parallel and distributed systems", Març 2019, vol. 30, núm. 3, p. 486-500.
Keywords
Data analytics, Real-time, Stream processing
Group of research
CAP - High Performace Computing Group

Participants

  • Villalba Navarro, Álvaro  (author)
  • Berral Garcia, Josep Lluis  (author)
  • Carrera Perez, David  (author)

Attachments