Decentralized microservice workflows for coalition environments

Simpkin, Chris; Taylor, Ian; Bent, Graham; Mel, Geeth de; Rallapalli, Swati

doi:10.1109/uic-atc.2017.8397424

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…In the context of MDO operations, the sensors and services will have been developed and owned by different MDO partners and standard centralized approaches, using formal ontology's to facilitate service definition and service matching for configuring applications are unlikely to work. The use of semantic vector representations to describe the sensors and services offers an alternative paradigm and in [2][3][4] it has been shown that service definition, service matching and decentralized service composition can be achieved using an approach based on Vector Symbolic Architectures (VSAs) and that the approach offers significant advantages in environments where communication is limited or unreliable.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Bent

Simpkin

Taylor

et al. 2021

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III

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Future Multi-Domain Operations (MDO) will require the coordination of hundreds-even thousands-of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different workflow configurations, thereby creating the applications necessary to support changing mission needs. To meet these objectives, we envision a distributed Cognitive Computing System (CCS) that consists of humans and software that work together as a 'Distributed Federated Brain'. Motivated by neuromorphic processing models, we present an approach that uses hyperdimensional symbolic semantic vector representations of the services/devices and workflows. We show how these can be used to perform decentralized service/device discovery and workflow composition in the context of a dynamic communications re-planning scenario. In this paper, we describe how emerging analogue AI 'In Memory' and 'Near Memory' computing can be used to efficiently perform some of the required hyperdimensional vector computation (HDC). We present an evaluation of the performance of an energy-efficient phase change memory device (PCM) that can perform the required vector operations and discuss how such devices could be used in energy-critical 'edge of network' tactical MDO operations.

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Section: Introductionmentioning

confidence: 99%

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Bent

Simpkin

Taylor

et al. 2021

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III

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“…• {𝑍 1 , 𝑍 2 , 𝑍 3 , … 𝑍 𝑛 } are the sub-feature vectors being combined for the individual nodes of Figure 1. Each 𝑍 𝑛 itself can be a compound vector representing a sub-workflow or a complex vector description for an individual service step, built using the methods described by Simpkin et al; 21 • 𝑝 0 , 𝑝 1 , 𝑝 2 , … are a set of known atomic role vectors used to define the current position or step in the workflow.…”

Section: Vsa Operationsmentioning

confidence: 99%

“…It was demonstrated that this can be performed efficiently using 10,000 bit BSC HVs. The vector library size in these type of applications depends on the number of IoBT services in the network with each service comparing its own service description vector(s) with the unbound workflow vector to determine if they are candidates to perform the next action required 13,14,20,21 . This vector comparison is performed in parallel by all the IoBT services and so the clean-up memory library is essentially distributed across the communications network.…”

Section: Bsc Vs Shv Bundling Capacitymentioning

confidence: 99%

Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations

Bent

Simpkin

Li³

et al. 2022

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV

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“…Equations ( 11) and (12) show the state of the workflow vector after the first and second unbinding. Only Z 1 is visible in Eq.…”

Section: Describing Workflows Using Vector Symbolic Architecturementioning

confidence: 99%

“…In previous work [1,5,10,11,12], we have addressed this challenge by making use of a Vector Symbolic Architecture (VSA) [13,14,15,16] to encode functional representations of micro-services and workflows into symbolic semantic vector representations. In the previous implementations, we used 10,000 bit binary vectors to represent service descriptions and a hierarchical binding scheme to represent workflows.…”

Section: Introductionmentioning

confidence: 99%

Efficient orchestration of Node-RED IoT workflows using a Vector Symbolic Architecture

Simpkin

Taylor

Harborne

et al. 2020

Future Generation Computer Systems

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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.

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Decentralized microservice workflows for coalition environments

Cited by 4 publications

References 21 publications

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations

Efficient orchestration of Node-RED IoT workflows using a Vector Symbolic Architecture

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