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On Quality of Service Optimization with Discrete QoS Options (1999)
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| Venue: | In Proceedings of the IEEE Real-time Technology and Applications Symposium |
| Citations: | 96 - 4 self |
BibTeX
@INPROCEEDINGS{Lee99onquality,
author = {Chen Lee and John Lehoczky and Ragunathan (raj Rajkumar and Dan Siewiorek},
title = {On Quality of Service Optimization with Discrete QoS Options},
booktitle = {In Proceedings of the IEEE Real-time Technology and Applications Symposium},
year = {1999},
pages = {276--286}
}
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Abstract
We present a QoS management framework that enables us to quantitatively measure QoS, and to analytically plan and allocate resources. In this model, end users ’ quality preferences are considered when system resources are apportioned across multipleapplicationssuch that the net utility that accrues to the end-users is maximized. In [23][24], we primarily worked with continuous QoS dimensions, and assumed that the utility gained by improvements along a QoS dimension were always representable by concave functions. In this paper, we relax both assumptions. One, we support discrete QoS operating points. Two, we make no assumptions about the concavity of the utility functions. Using these as the basis, we tackle the problem of maximizing system utility by allocating a single finite resource to satisfy the QoS requirements of multiple applications along multiple QoS dimensions. We present two near-optimal algorithms to solve this problem. The first yields an allocation within a known bounded distance from the optimal solution, and the second yields an allocation whose distance from the optimal solution can be explicitly controlled by the QoS manager. We compare the run-times of these near-optimal algorithms and their solution quality relative to the optimal allocation, which in turn is computed using dynamic programming. These detailed evaluations provide practical insight into which of these algorithms can be used online in real-time systems.
Keyphrases
discrete qos option service optimization optimal solution near-optimal algorithm utility function concave function continuous qos dimension second yield end user quality preference multiple application system utility qos dimension system resource detailed evaluation qos manager discrete qos real-time system first yield practical insight solution quality optimal allocation qos requirement dynamic programming qos management framework net utility multiple qos dimension bounded distance single finite resource
