Trigger-based Incremental Data Processing with Unified Sync and Async Model

Dong Dai, Yong Chen, Dries Kimpe, Rob Ross

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usability. In this study, we propose a trigger-based incremental computing framework for big data applications in the cloud, called Domino, with both synchronous and asynchronous mechanism to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.

Original languageEnglish
JournalIEEE Transactions on Cloud Computing
DOIs
StateAccepted/In press - Jun 20 2018

Keywords

  • Cloud
  • Incremental Computing
  • Programming Framework

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