Developing applications in large scale, dynamic fog computing: A case study. (21st April 2019)

• Record Type: Journal Article
• Authors: Giang, Nam Ky; Lea, Rodger; Leung, Victor C.M.
• Other Names: Ranjan Rajiv guestEditor.; Villari Massimo guestEditor.; Shen Haiying guestEditor.; Rana Omer guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 5(2020)
• Page Start: 519
• Page End: 532
• Publication Date: 2019-04-21
• Subjects: coordination -- dataflow -- dynamic -- edge/fog computing -- exogenous -- large scale; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2695
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: In recent years, fog computing has emerged as a new distributed system model for a large class of applications that are data‐intensive or delay‐sensitive. By exploiting widely distributed computing infrastructure that is located closer to the network edge, communication cost and service response time can be significantly reduced. However, developing this class of applications is not straightforward and requires addressing three key challenges, ie, supporting the dynamic nature of the edge network, managing the context‐dependent characteristics of application logic, and dealing with the large scale of the system. In this paper, we present a case study in building fog computing applications using our open source platform Distributed Node‐RED (DNR). In particular, we show how applications can be decomposed and deployed to a geographically distributed infrastructure using DNR, and how existing software components can be adapted and reused to participate in fog applications. We present a lab‐based implementation of a fog application built using DNR that addresses the first two of the issues highlighted earlier. To validate that our approach also deals with large scale, we augment our live trial with a large scale simulation of the application model, conducted in Omnet++, which shows the scalability of the model and how it supports the dynamic nature of fog applications.