Improving the Mean-Field Fluid Model of Processor Sharing Queueing Networks for Dynamic Performance Models in Cloud Computing
Ruuskanen, Johan LU
; Berner, Tommi
LU
; Årzén, Karl-Erik
LU
and Cervin, Anton
LU
(2021)
In Performance Evaluation
151.
- Abstract
- Resource management in cloud computing is a difficult problem, as one is often tasked with balancing between adequate service to clients and cost minimization in dynamic environments of many interconnected components. To make correct decisions in these environments, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many real-time applications, different approximation methods for important metrics are frequently employed. One such method—that provides both transient solutions and short, scalable computation times—is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential... (More)
- Resource management in cloud computing is a difficult problem, as one is often tasked with balancing between adequate service to clients and cost minimization in dynamic environments of many interconnected components. To make correct decisions in these environments, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many real-time applications, different approximation methods for important metrics are frequently employed. One such method—that provides both transient solutions and short, scalable computation times—is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential equations. However, finding a fluid model that can adequately approximate an arbitrary queueing network is in general difficult. In this paper, we extend the state of the art with the following three contributions. First, we show that for any mixed multiclass queueing network of processor sharing and delay queues with phase-type service time distributions, such a fluid model can be found via the mean-field approximation. Furthermore, we propose an improved model based on smoothing of the processor share function that improves the performance of certain systems. Finally, using the smoothed mean-field model, we introduce an accurate closed-form approximation of the response time CDF over any subset of classes and queues. The contributions are further evaluated in a large simulation experiment, which shows that they can be used to accurately predict performance metrics under some system perturbations common in cloud computing. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8ae96e1e-0bc7-4774-990d-42d5187cbf36
- author
- Ruuskanen, Johan
LU
; Berner, Tommi
LU
; Årzén, Karl-Erik
LU
and Cervin, Anton
LU
- organization
- publishing date
- 2021-09-23
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Queueing network, Processor sharing, Mean-field approximation, Fluid model, Response time approximation
- in
- Performance Evaluation
- volume
- 151
- article number
- 102231
- publisher
- Elsevier
- external identifiers
-
- scopus:85116545663
- ISSN
- 0166-5316
- DOI
- 10.1016/j.peva.2021.102231
- project
- Event-Based Information Fusion for the Self-Adaptive Cloud
- language
- English
- LU publication?
- yes
- id
- 8ae96e1e-0bc7-4774-990d-42d5187cbf36
- date added to LUP
- 2021-10-11 07:28:51
- date last changed
- 2023-05-09 15:07:14
@article{8ae96e1e-0bc7-4774-990d-42d5187cbf36,
abstract = {{Resource management in cloud computing is a difficult problem, as one is often tasked with balancing between adequate service to clients and cost minimization in dynamic environments of many interconnected components. To make correct decisions in these environments, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many real-time applications, different approximation methods for important metrics are frequently employed. One such method—that provides both transient solutions and short, scalable computation times—is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential equations. However, finding a fluid model that can adequately approximate an arbitrary queueing network is in general difficult. In this paper, we extend the state of the art with the following three contributions. First, we show that for any mixed multiclass queueing network of processor sharing and delay queues with phase-type service time distributions, such a fluid model can be found via the mean-field approximation. Furthermore, we propose an improved model based on smoothing of the processor share function that improves the performance of certain systems. Finally, using the smoothed mean-field model, we introduce an accurate closed-form approximation of the response time CDF over any subset of classes and queues. The contributions are further evaluated in a large simulation experiment, which shows that they can be used to accurately predict performance metrics under some system perturbations common in cloud computing.}},
author = {{Ruuskanen, Johan and Berner, Tommi and Årzén, Karl-Erik and Cervin, Anton}},
issn = {{0166-5316}},
keywords = {{Queueing network; Processor sharing; Mean-field approximation; Fluid model; Response time approximation}},
language = {{eng}},
month = {{09}},
publisher = {{Elsevier}},
series = {{Performance Evaluation}},
title = {{Improving the Mean-Field Fluid Model of Processor Sharing Queueing Networks for Dynamic Performance Models in Cloud Computing}},
url = {{http://dx.doi.org/10.1016/j.peva.2021.102231}},
doi = {{10.1016/j.peva.2021.102231}},
volume = {{151}},
year = {{2021}},
}