CLC number: TP31
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-04-27
Cited: 0
Clicked: 6594
Yong ZHANG, Shen-sheng ZHANG, Song-qiao HAN. Adaptive service configuration approach for quality of service management in ubiquitous computing environments[J]. Journal of Zhejiang University Science A, 2009, 10(7): 964-975.
@article{title="Adaptive service configuration approach for quality of service management in ubiquitous computing environments",
author="Yong ZHANG, Shen-sheng ZHANG, Song-qiao HAN",
journal="Journal of Zhejiang University Science A",
volume="10",
number="7",
pages="964-975",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820422"
}
%0 Journal Article
%T Adaptive service configuration approach for quality of service management in ubiquitous computing environments
%A Yong ZHANG
%A Shen-sheng ZHANG
%A Song-qiao HAN
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 7
%P 964-975
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820422
TY - JOUR
T1 - Adaptive service configuration approach for quality of service management in ubiquitous computing environments
A1 - Yong ZHANG
A1 - Shen-sheng ZHANG
A1 - Song-qiao HAN
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 7
SP - 964
EP - 975
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820422
Abstract: Mobility and resource-limitedness pose challenging issues to service configuration for quality of service (QoS) management in ubiquitous computing environments. Previous configuration approaches, such as static resource reservation, dynamic resource allocation and single service composition are not valid in the environments. In this study, we present an adaptive service configuration approach. Firstly, we reduce the dynamic configuration process to a control model which aims to achieve the variation of critical QoS on minimal level with less resource cost. Secondly, to deal with different QoS variations, we design two configuration strategies—service chain reconfiguration and QoS parameter adjustment—and implement them based on fuzzy logic control theory. Finally, a configuration algorithm is developed to flexibly employ the two configuration strategies in tune with the error of critical QoS in configuration process. The results of simulation experiments suggest that our approach outperforms existing configuration approaches in both QoS improvement and resource utilization.
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