Efficient orchestration of Node-RED IoT workflows using a vector symbolic architecture

Simpkin, Chris, Taylor, Ian ORCID: https://orcid.org/0000-0001-5040-0772, Harborne, Daniel, Bent, Graham, Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057 and Ganti, Raghu K. 2020. Efficient orchestration of Node-RED IoT workflows using a vector symbolic architecture. Future Generation Computer Systems 111 , pp. 117-131. 10.1016/j.future.2020.04.005

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Abstract

Numerous workflow systems span multiple scientific domains and environments, and for the Internet of Things (IoT), Node-RED offers an attractive Web based user interface to execute IoT service-based workflows. However, like most workflow systems, it coordinates the workflow centrally, and cannot run within more transient environments where nodes are mobile. To address this gap, we show how Node-RED workflows can be migrated into a decentralized execution environment for operation on mobile ad-hoc networks, and we demonstrate this by converting a Node-RED based traffic congestion detection workflow to operate in a decentralized environment. The approach uses a Vector Symbolic Architecture (VSA) to dynamically convert Node-Red applications into a compact semantic vector representation that encodes the service interfaces and the workflow in which they are embedded. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected into the required workflow in a completely decentralized manner. The resulting system provides a convenient environment where the Node-RED front-end graphical composition tool can be used to orchestrate decentralized workflows. In this paper, we further extend this work by introducing a new dynamic VSA vector compression scheme that compresses vectors for on-the-wire communication, thereby reducing communication bandwidth while maintaining the semantic information content. This algorithm utilizes the holographic properties of the symbolic vectors to perform compression taking into consideration the number of combined vectors along with similarity bounds that determine conflict with other encoded vectors used in the same context. The resulting savings make this approach extremely efficient for discovery in service-based decentralized workflows.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	Decentralized Workflows, Vector Symbolic Architecture, Machine Learning, Dynamic Wireless Networks, P2P
Publisher:	Elsevier
ISSN:	0167-739X
Funders:	DIAS ITA
Related URLs:	Publisher
Date of First Compliant Deposit:	7 May 2020
Date of Acceptance:	2 April 2020
Last Modified:	17 Nov 2024 04:00
URI:	https://orca.cardiff.ac.uk/id/eprint/131522

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