A Mixture Density Network approach to predicting response times in layered systems
File(s)
Accepted version
Author(s)
Niu, Zifeng
Casale, Giuliano
Type
Conference Paper
Abstract
Layering is a common feature in modern service-based systems. The characterization of response times in a layered system is an important but challenging analysis dimension inQuality of Service (QoS) assessment. In this paper, we develop anovel approach to estimate the mean and variance of response time in systems that may be abstracted as layered queueing networks. The core step of the method is to obtain the response time distributions in the submodels that are used to analyze the layered queueing networks by means of decomposition. We model the conditional response time distribution as a mixture of Gamma density functions for which we learn the parametersby means of a Mixture Density Network (MDN). The scheme recursively propagates the MDN predictions through the layersusing phase-type distributions and performs convolutions togain the approximation of the system delay. The experimental results show an accurate match between simulations and MDN predictions and also verify the effectiveness of the approach.
Date Issued
2022-11-22
Date Acceptance
2021-09-05
Citation
2022, pp.1-8
Publisher
IEEE
Start Page
1
End Page
8
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9614286
Source
IEEE MASCOTS 2021
Publication Status
Published
Start Date
2021-11-03
Finish Date
2021-11-05
Coverage Spatial
Virtual
Date Publish Online
2021-11-22