Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A Scalable Vector Symbolic Architecture Approach for Decentralized Workflows

Simpkin, Christopher, Taylor, Ian, Bent, Graham, de Mel, Geeth and Ganti, Raghu 2018. A Scalable Vector Symbolic Architecture Approach for Decentralized Workflows. Presented at: The Eighth International Conference on Advanced Collaborative Networks, Systems and Applications COLLA 2018, Venice, Italy, 24-28 June, 2018.

[img]
Preview
PDF - Accepted Post-Print Version
Download (1MB) | Preview

Abstract

Vectors Symbolic Architectures (VSAs) are distributed representations that combine random patterns, representing atomic symbols across a hyper-dimensional vector space, into new symbolic vector representations that semantically represent the component vectors and their relationships. In this paper, we extend the VSA approach and apply it to decentralized workflows, capable of executing distributed compute nodes and their interdependencies. To achieve this goal, services must be discovered and orchestrated in a decentralized way with the minimum communication overhead whilst providing detailed information about the workflow - tasks, dependencies, location, metadata, and so on. To this end, we extended VSAs using a hierarchical vector chunking scheme that enables semantic matching at each level and provides scaling up to tens of thousands of services. We then show how VSAs can be used to encode complex workflows by building primitives that represent sequences (pipelines) and then extend this to support full Directed Acyclic Graphs (DAGs) and apply this to five well-known Pegasus scientific workflows to demonstrate the approach

Item Type: Conference or Workshop Item (Paper)
Date Type: Acceptance
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Funders: U.S. Army Research Laboratory, U.K. Ministry of Defence
Date of First Compliant Deposit: 20 June 2018
Date of Acceptance: 7 May 2018
Last Modified: 29 Aug 2019 21:14
URI: http://orca-mwe.cf.ac.uk/id/eprint/112242

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics