Real-time edge framework (RTEF): task scheduling and realisation

Volkan Gezer () and Achim Wagner ()
Additional contact information
Volkan Gezer: German Research Center for Artificial Intelligence (DFKI)
Achim Wagner: German Research Center for Artificial Intelligence (DFKI)

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 8, No 15, 2317 pages

Abstract: Abstract With the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing power close to the source also enables real-time applications. In this paper, we propose a novel software reference architecture for Edge Servers, which is operating system (OS) and hardware-agnostic. Edge Servers can collaborate and execute (near) real-time tasks on time, either by downscaling or scheduling them according to their deadlines or offloading them to other Edge Servers in the network. Decision making for offloading, resource planning, and task scheduling are challenging problems in decentralized systems. The paper explains how resource planning and task scheduling can be overcome with software approach. Finally, the article realises the architecture as a framework, called Real-Time Edge Framework (RTEF) and validates its correctness with a use case.

Keywords: Real-time computing; Edge computing; Task offloading; Edge in manufacturing; Fog computing (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01760-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01760-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-021-01760-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().