Details
| Original language | English |
|---|---|
| Title of host publication | 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC) |
| Editors | Alexandru Iosup, Radu Prodan, Alexandru Uta, Florin Pop |
| Pages | 58-65 |
| Number of pages | 8 |
| ISBN (electronic) | 9781728138015 |
| Publication status | Published - 2019 |
| Externally published | Yes |
| Event | 18th International Symposium on Parallel and Distributed Computing, ISPDC 2019 - , Netherlands Duration: 5 Jun 2019 → 7 Jun 2019 |
Abstract
Keywords
- Big Data, Machine Learning, Systems, Performance, Efficiency, Big Data, Machine Learning, Systems, Performance, Efficiency
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
Cite this
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2019 18th International Symposium on Parallel and Distributed Computing (ISPDC). ed. / Alexandru Iosup; Radu Prodan; Alexandru Uta; Florin Pop. 2019. p. 58-65 8790842.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - The coming age of pervasive data processing
AU - Rellermeyer, Jan
AU - Omranian Khorasani, Sobhan
AU - Graur, Dan
AU - Parthasarathy, Apourva
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Big Data
KW - Machine Learning
KW - Systems
KW - Performance
KW - Efficiency
KW - Big Data, Machine Learning, Systems, Performance, Efficiency
UR - http://www.scopus.com/inward/record.url?scp=85071448586&partnerID=8YFLogxK
U2 - 10.1109/ISPDC.2019.00011
DO - 10.1109/ISPDC.2019.00011
M3 - Conference contribution
SN - 9781728138022
SP - 58
EP - 65
BT - 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)
A2 - Iosup, Alexandru
A2 - Prodan, Radu
A2 - Uta, Alexandru
A2 - Pop, Florin
T2 - 18th International Symposium on Parallel and Distributed Computing, ISPDC 2019
Y2 - 5 June 2019 through 7 June 2019
ER -