Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems

David W. McKEE; Xue OUYANG; Jie XU

doi:10.1587/transcom.2017ADI0001

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Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems

David W. McKEE, Xue OUYANG, Jie XU

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With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.

Publication: IEICE TRANSACTIONS on Communications Vol.E101-B No.8 pp.1760-1767

Publication Date: 2018/08/01

Publicized: 2018/02/22

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2017ADI0001

Type of Manuscript: Special Section INVITED PAPER (Special Section on Autonomous Decentralized Systems Technologies and Approaches Innovation through Structure Change of Society and Life)

Category

Authors

David W. McKEE
  University of Leeds
Xue OUYANG
  University of Leeds
Jie XU
  University of Leeds

Keyword

cloud, SOA, services, autonomous systems, real-time, straggler, IoT, IoS, simulation

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David W. McKEE, Xue OUYANG, Jie XU, "Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems" in IEICE TRANSACTIONS on Communications, vol. E101-B, no. 8, pp. 1760-1767, August 2018, doi: 10.1587/transcom.2017ADI0001.
Abstract: With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017ADI0001/_p

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@ARTICLE{e101-b_8_1760,
author={David W. McKEE, Xue OUYANG, Jie XU, },
journal={IEICE TRANSACTIONS on Communications},
title={Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems},
year={2018},
volume={E101-B},
number={8},
pages={1760-1767},
abstract={With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.},
keywords={},
doi={10.1587/transcom.2017ADI0001},
ISSN={1745-1345},
month={August},}

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TY - JOUR
TI - Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 1760
EP - 1767
AU - David W. McKEE
AU - Xue OUYANG
AU - Jie XU
PY - 2018
DO - 10.1587/transcom.2017ADI0001
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2018
AB - With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.
ER -