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SmartOrch: an adaptive orchestration system for human-machine collectives

Published: 03 April 2017 Publication History

Abstract

Web-based collaborative systems, where most computation is performed by human collectives, have distinctly different requirements from traditional workflow orchestration systems, as humans have to be mobilised to perform computations and the system has to adapt to their collective behaviour at runtime. In this paper, we present a social orchestration system called SmartOrch, which has been designed specifically for collective adaptive systems in which human participation is at the core of the overall distributed computation. SmartOrch provides a flexible and customisable workflow composition framework that has multi-level optimisation capabilities. These features allow us to manage the uncertainty that collective adaptive systems need to deal with in a principled way.
We demonstrate the benefits of SmartOrch with simulation experiments in a ridesharing domain. Our experiments show that SmartOrch is able to respond flexibly to variation in collective human behaviour, and to adapt to observed behaviour at different levels. This is accomplished by learning how to propose and route human-based tasks, how to allocate computational resources when managing these tasks, and how to adapt the overall interaction model of the platform based on past performance. By proposing novel, solid engineering principles for these kinds of systems, SmartOrch addresses shortcomings of previous work that mostly focused on application-specific, non-adaptive solutions.

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Cited By

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  • (2022)A Low-Code Framework for Complex Crowdsourcing Work Based on Process ModelingComputational Intelligence and Neuroscience10.1155/2022/94967412022(1-14)Online publication date: 29-Apr-2022
  • (2019)Using Task Descriptions with Explicit Representation of Allocation of Functions, Authority and Responsibility to Design and Assess AutomationHuman Work Interaction Design. Designing Engaging Automation10.1007/978-3-030-05297-3_3(36-56)Online publication date: 1-Jan-2019
  • (2017)A Programming Model for Hybrid Collaborative Adaptive SystemsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2017.2702578(1-1)Online publication date: 2017

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cover image ACM Conferences
SAC '17: Proceedings of the Symposium on Applied Computing
April 2017
2004 pages
ISBN:9781450344869
DOI:10.1145/3019612
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 03 April 2017

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Author Tags

  1. collective adaptive systems
  2. distributed systems
  3. workflow composition
  4. workflow optimisation
  5. workflow orchestration

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SAC 2017
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SAC 2017: Symposium on Applied Computing
April 3 - 7, 2017
Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2022)A Low-Code Framework for Complex Crowdsourcing Work Based on Process ModelingComputational Intelligence and Neuroscience10.1155/2022/94967412022(1-14)Online publication date: 29-Apr-2022
  • (2019)Using Task Descriptions with Explicit Representation of Allocation of Functions, Authority and Responsibility to Design and Assess AutomationHuman Work Interaction Design. Designing Engaging Automation10.1007/978-3-030-05297-3_3(36-56)Online publication date: 1-Jan-2019
  • (2017)A Programming Model for Hybrid Collaborative Adaptive SystemsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2017.2702578(1-1)Online publication date: 2017

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