Decision-Driven Execution: A Distributed Resource Management Paradigm for the Age of IoT

Tarek Abdelzaher, Md Tanvir A. Amin, Amotz Bar-Noy, William Dron, Ramesh Govindan, Reginald Hobbs, Shaohan Hu, Jung Eun Kim, Jongdeog Lee, Kelvin Marcus, Shuochao Yao, Yiran Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1825-1835
Number of pages11
ISBN (Electronic)9781538617915
DOIs
StatePublished - Jul 13 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems

Other

Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
CountryUnited States
CityAtlanta
Period6/5/176/8/17

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Keywords

  • Decision-driven Execution
  • Distributed Computing Paradigms
  • IoT
  • Learning
  • Sensor Networks

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Abdelzaher, T., Amin, M. T. A., Bar-Noy, A., Dron, W., Govindan, R., Hobbs, R., Hu, S., Kim, J. E., Lee, J., Marcus, K., Yao, S., & Zhao, Y. (2017). Decision-Driven Execution: A Distributed Resource Management Paradigm for the Age of IoT. In K. Lee, & L. Liu (Eds.), Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017 (pp. 1825-1835). [7980121] (Proceedings - International Conference on Distributed Computing Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2017.318