Ahab: A cloud‐based distributed big data analytics framework for the Internet of Things. (4th July 2016)

• Record Type: Journal Article
• Authors: Vögler, Michael; Schleicher, Johannes M.; Inzinger, Christian; Dustdar, Schahram
• Other Names: Ranjan Rajiv guestEditor.; Wang Lizhe guestEditor.; Jayaraman Prem Prakash guestEditor.; Mitra Karan guestEditor.; Georgakopoulos Dimitrios guestEditor.
• Is Part Of: Software, practice & experience. Volume 47:Number 3(2017)
• Page Start: 443
• Page End: 454
• Publication Date: 2016-07-04
• Subjects: big data -- smart city -- internet of things -- stream processing -- lambda architecture; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2424
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Smart city applications generate large amounts of operational data during their execution, such as information from infrastructure monitoring, performance and health events from used toolsets, and application execution logs. These data streams contain vital information about the execution environment that can be used to fine‐tune or optimize different layers of a smart city application infrastructure. Current approaches do not sufficiently address the efficient collection, processing, and storage of this information in the smart city domain. In this paper, we presentAhab, a generic, scalable, and fault‐tolerant data processing framework based on the cloud that allows operators to perform online and offline analyses on gathered data to better understand and optimize the behavior of the available smart city infrastructure.Ahab is designed for easy integration of new data sources, provides an extensible API to perform custom analysis tasks, and a domain‐specific language to define adaptation rules based on analysis results. We demonstrate the feasibility of the proposed approach using an example application for autonomous intersection management in smart city environments. Our framework is able to autonomously optimize application deployment topologies by distributing processing load over available infrastructure resources when necessary based on both online analysis of the current state of the environment and patterns learned from historical data. Copyright © 2016 John Wiley & Sons, Ltd.