The Coming Age of Pervasive Data Processing

TL;DR

This paper discusses the future of pervasive data processing, emphasizing the need for new scalable systems to handle diverse devices and workloads efficiently in the era of Big Data and machine learning.

Contribution

It identifies challenges and proposes directions for designing next-generation large-scale data processing systems suitable for a wide range of devices.

Findings

Highlights inefficiencies in current data processing platforms.

Proposes new directions for scalable, efficient systems.

Addresses challenges across diverse hardware environments.

Abstract

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.