Characterizing application scheduling on edge, fog, and cloud computing resources. (30th April 2019)

• Record Type: Journal Article
• Authors: Varshney, Prateeksha; Simmhan, Yogesh
• 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: 558
• Page End: 595
• Publication Date: 2019-04-30
• Subjects: application models -- cloud computing -- edge computing -- fog computing -- quality of service -- scheduling algorithms; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2699
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide‐area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality‐of‐service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.