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 publication18th IEEE International Symposium on Parallel and Distributed Computing
Publication statusAccepted/In press - 2019
EventIEEE International Symposium on Parallel and Distributed Computing - Amsterdam, Netherlands
Duration: 5 Jun 20197 Jun 2019
Conference number: 2019
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Conference

ConferenceIEEE International Symposium on Parallel and Distributed Computing
Abbreviated titleISPDC
CountryNetherlands
CityAmsterdam
Period5/06/197/06/19
Internet address

ID: 53959317