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Multi-step ahead response time prediction for single server queuing systems

Amani, Payam LU ; Kihl, Maria LU and Robertsson, Anders LU (2011) The 16th IEEE Symposium on Computers and Communications (ISCC) In [Host publication title missing]
Abstract
Multi-step ahead response time prediction of CPU

constrained computing systems is vital for admission control,

overload protection and optimization of resource allocation in

these systems. CPU constrained computing systems such as web

servers can be modeled as single server queuing systems. These

systems are stochastic and nonlinear. Thus, a well-designed nonlinear

prediction scheme would be able to represent the dynamics

of such a system much better than a linear scheme. A nonlinear

autoregressive neural network with exogenous inputs based

multi-step ahead response time predictor has been developed.

The proposed estimator has many promising... (More)
Multi-step ahead response time prediction of CPU

constrained computing systems is vital for admission control,

overload protection and optimization of resource allocation in

these systems. CPU constrained computing systems such as web

servers can be modeled as single server queuing systems. These

systems are stochastic and nonlinear. Thus, a well-designed nonlinear

prediction scheme would be able to represent the dynamics

of such a system much better than a linear scheme. A nonlinear

autoregressive neural network with exogenous inputs based

multi-step ahead response time predictor has been developed.

The proposed estimator has many promising characteristics that

make it a viable candidate for being implemented in admission

control products for computing systems. It has a simple structure,

is nonlinear, supports multi-step ahead prediction, and works

very well under time variant and non-stationary scenarios such

as single server queuing systems under time varying mean arrival

rate. Performance of the proposed predictor is evaluated through

simulation. Simulations show that the proposed predictor is able

to predict the response times of single server queuing systems in

multi-step ahead with very good precision represented by very

small mean absolute and mean squared prediction errors. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
The 16th IEEE Symposium on Computers and Communications (ISCC)
external identifiers
  • WOS:000298614900165
  • Scopus:80052720751
project
LCCC
Broadband Communications: Modelling and Control of Server Systems
language
English
LU publication?
yes
id
0fe3f77a-16f0-46d6-846e-5d484e181f0c (old id 2018675)
date added to LUP
2011-07-04 12:57:15
date last changed
2016-10-13 04:50:23
@misc{0fe3f77a-16f0-46d6-846e-5d484e181f0c,
  abstract     = {Multi-step ahead response time prediction of CPU<br/><br>
constrained computing systems is vital for admission control,<br/><br>
overload protection and optimization of resource allocation in<br/><br>
these systems. CPU constrained computing systems such as web<br/><br>
servers can be modeled as single server queuing systems. These<br/><br>
systems are stochastic and nonlinear. Thus, a well-designed nonlinear<br/><br>
prediction scheme would be able to represent the dynamics<br/><br>
of such a system much better than a linear scheme. A nonlinear<br/><br>
autoregressive neural network with exogenous inputs based<br/><br>
multi-step ahead response time predictor has been developed.<br/><br>
The proposed estimator has many promising characteristics that<br/><br>
make it a viable candidate for being implemented in admission<br/><br>
control products for computing systems. It has a simple structure,<br/><br>
is nonlinear, supports multi-step ahead prediction, and works<br/><br>
very well under time variant and non-stationary scenarios such<br/><br>
as single server queuing systems under time varying mean arrival<br/><br>
rate. Performance of the proposed predictor is evaluated through<br/><br>
simulation. Simulations show that the proposed predictor is able<br/><br>
to predict the response times of single server queuing systems in<br/><br>
multi-step ahead with very good precision represented by very<br/><br>
small mean absolute and mean squared prediction errors.},
  author       = {Amani, Payam and Kihl, Maria and Robertsson, Anders},
  language     = {eng},
  publisher    = {ARRAY(0xba116e8)},
  series       = {[Host publication title missing]},
  title        = {Multi-step ahead response time prediction for single server queuing systems},
  year         = {2011},
}