Documents

  • 08790842

    Final published version, 1 MB, PDF-document

DOI

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.

Original languageEnglish
Title of host publication2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)
Subtitle of host publicationProceedings
EditorsAlexandru Iosup, Florin Pop, Radu Prodan, Alexandru Uta
PublisherIEEE
Pages58-65
Number of pages8
ISBN (Electronic)978-1-7281-3801-5
ISBN (Print)978-1-7281-3802-2
DOIs
Publication statusPublished - 2019
Event18th International Symposium on Parallel and Distributed Computing, ISPDC 2019 - Amsterdam, Netherlands
Duration: 5 Jun 20197 Jun 2019

Conference

Conference18th International Symposium on Parallel and Distributed Computing, ISPDC 2019
CountryNetherlands
CityAmsterdam
Period5/06/197/06/19

    Research areas

  • Big Data, Machine Learning, Systems, Performance, Efficiency

ID: 56882541