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

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.

[1]  Péter Kacsuk,et al.  P‐GRADE portal family for grid infrastructures , 2011, Concurr. Comput. Pract. Exp..

[2]  Ian Taylor,et al.  Dynamic Distributed Orchestration of Node-RED IoT Workflows Using a Vector Symbolic Architecture , 2018, 2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS).

[3]  T.R. Henderson,et al.  CORE: A real-time network emulator , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[4]  Tony A. Plate,et al.  Holographic Reduced Representation: Distributed Representation for Cognitive Structures , 2003 .

[5]  William Shakespeare,et al.  The tragedy of Hamlet , 1899 .

[6]  Lambert M. Surhone,et al.  Node.js , 2010 .

[7]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[8]  P. Kanerva,et al.  Permutations as a means to encode order in word space , 2008 .

[9]  Pentti Kanerva,et al.  Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors , 2009, Cognitive Computation.

[10]  David A. Maltz,et al.  A performance comparison of multi-hop wireless ad hoc network routing protocols , 1998, MobiCom '98.

[11]  Yolanda Gil,et al.  Workflow management in GriPhyN , 2004 .

[12]  Ross W. Gayler Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience , 2004, ArXiv.

[13]  Daniel S. Katz,et al.  Pegasus: A framework for mapping complex scientific workflows onto distributed systems , 2005, Sci. Program..

[14]  Denis Kleyko Pattern Recognition with Vector Symbolic Architectures , 2016 .

[15]  Michael N. Jones,et al.  Encoding Sequential Information in Semantic Space Models: Comparing Holographic Reduced Representation and Random Permutation , 2015, Comput. Intell. Neurosci..

[16]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[17]  Ian Taylor,et al.  Decentralized microservice workflows for coalition environments , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[18]  Rajiv Ranjan,et al.  G-Hadoop: MapReduce across distributed data centers for data-intensive computing , 2013, Future Gener. Comput. Syst..

[19]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[20]  Maurizio Giordano DNS-Based Discovery System in Service Oriented Programming , 2005, EGC.

[21]  Yogesh L. Simmhan,et al.  The Trident Scientific Workflow Workbench , 2008, 2008 IEEE Fourth International Conference on eScience.

[22]  Geoffrey E. Hinton Mapping Part-Whole Hierarchies into Connectionist Networks , 1990, Artif. Intell..

[23]  Matthew Shields,et al.  WS-RF Workflow in Triana , 2008, Int. J. High Perform. Comput. Appl..

[24]  Bartosz Balis Increasing Scientific Workflow Programming Productivity with HyperFlow , 2014, 2014 9th Workshop on Workflows in Support of Large-Scale Science.

[25]  Daniel S. Katz,et al.  Swift: A language for distributed parallel scripting , 2011, Parallel Comput..

[26]  Matthew R. Pocock,et al.  Taverna: a tool for the composition and enactment of bioinformatics workflows , 2004, Bioinform..

[27]  Joseph P. Macker,et al.  Orchestration and analysis of decentralized workflows within heterogeneous networking infrastructures , 2017, Future Gener. Comput. Syst..

[28]  Radu Prodan,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005, SGMD.

[29]  Bertram Ludäscher,et al.  Kepler: an extensible system for design and execution of scientific workflows , 2004 .

[30]  Ian J. Taylor,et al.  Triana Applications within Grid Computing and Peer to Peer Environments , 2003, Journal of Grid Computing.

[31]  Trevor Bekolay,et al.  A Large-Scale Model of the Functioning Brain , 2012, Science.

[32]  Geoffrey E. Hinton,et al.  Distributed representations and nested compositional structure , 1994 .

[33]  Ian Taylor,et al.  Constructing distributed time-critical applications using cognitive enabled services , 2019, Future Gener. Comput. Syst..

[34]  Daniel H. Steinberg,et al.  Zero Configuration Networking: The Definitive Guide , 2005 .

[35]  Javier Fabra,et al.  DENEB: a platform for the development and execution of interoperable dynamic Web processes , 2011, Concurr. Comput. Pract. Exp..

[36]  Omer Levy,et al.  word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method , 2014, ArXiv.

[37]  Ian Taylor,et al.  A Scalable Vector Symbolic Architecture Approach for Decentralized Workflows , 2018 .

[38]  Johan Montagnat,et al.  Flexible and Efficient Workflow Deployment of Data-Intensive Applications On Grids With MOTEUR , 2008, Int. J. High Perform. Comput. Appl..

[39]  Ehud Gudes,et al.  A Survey on Geographically Distributed Big-Data Processing Using MapReduce , 2017, IEEE Transactions on Big Data.