For cloud enterprise customers that require services on demand, data
centers must
allocate and partition data center resources in a dynamic fashion. We
consider the problem in which a request from an enterprise customer is
mapped to a virtual network (VN) that is allocated requiring both bandwidth
and compute resources by connecting it from an entry point of the
datacenter to one or more servers, should this data center be selected from
multiple geographically distributed data centers.
We present a dynamic traffic engineering framework, for which we develop
an optimization model based on mixed-integer linear programming (MIP)
formulation
that a data center operator can use is at each review point to optimally
assign VN customers.
Through a series of studies, we then present results on how different VN
customers are treated in terms of request acceptance when each VN class
have different resource requirement. We found that a VN class with low
resource requirement has a low blocking even in heavy traffic , while the
VN class with high resource requirement faces high service denial. On the
other hand, cost for the VN with the highest resource requirement is not
always the highest in the heavy traffic because of very high service denial
faced by this VN class.
%0 Conference Paper
%1 Maswood2016
%A Maswood, Mirza Mohd Shahriar
%A Develder, Chris
%A Madeira, Edmundo
%A Medhi, Deep
%B 28th International Teletraffic Congress (ITC 28)
%C Würzburg, Germany
%D 2016
%K itc itc28
%T Dynamic Virtual Network Traffic Engineering with Energy Efficiency in
Multi-Location Data Center Networks
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Maswood2016.pdf?inline=true
%X For cloud enterprise customers that require services on demand, data
centers must
allocate and partition data center resources in a dynamic fashion. We
consider the problem in which a request from an enterprise customer is
mapped to a virtual network (VN) that is allocated requiring both bandwidth
and compute resources by connecting it from an entry point of the
datacenter to one or more servers, should this data center be selected from
multiple geographically distributed data centers.
We present a dynamic traffic engineering framework, for which we develop
an optimization model based on mixed-integer linear programming (MIP)
formulation
that a data center operator can use is at each review point to optimally
assign VN customers.
Through a series of studies, we then present results on how different VN
customers are treated in terms of request acceptance when each VN class
have different resource requirement. We found that a VN class with low
resource requirement has a low blocking even in heavy traffic , while the
VN class with high resource requirement faces high service denial. On the
other hand, cost for the VN with the highest resource requirement is not
always the highest in the heavy traffic because of very high service denial
faced by this VN class.
@inproceedings{Maswood2016,
abstract = {For cloud enterprise customers that require services on demand, data
centers must
allocate and partition data center resources in a dynamic fashion. We
consider the problem in which a request from an enterprise customer is
mapped to a virtual network (VN) that is allocated requiring both bandwidth
and compute resources by connecting it from an entry point of the
datacenter to one or more servers, should this data center be selected from
multiple geographically distributed data centers.
We present a dynamic traffic engineering framework, for which we develop
an optimization model based on mixed-integer linear programming (MIP)
formulation
that a data center operator can use is at each review point to optimally
assign VN customers.
Through a series of studies, we then present results on how different VN
customers are treated in terms of request acceptance when each VN class
have different resource requirement. We found that a VN class with low
resource requirement has a low blocking even in heavy traffic , while the
VN class with high resource requirement faces high service denial. On the
other hand, cost for the VN with the highest resource requirement is not
always the highest in the heavy traffic because of very high service denial
faced by this VN class.},
added-at = {2020-04-29T16:57:37.000+0200},
address = {Würzburg, Germany},
author = {Maswood, Mirza Mohd Shahriar and Develder, Chris and Madeira, Edmundo and Medhi, Deep},
biburl = {https://www.bibsonomy.org/bibtex/28fb7bc68903ce7d861c0e4c9110d722b/itc},
booktitle = {28th International Teletraffic Congress (ITC 28)},
days = {12},
interhash = {12f16e2878cb9cfa46f422a8801b37b7},
intrahash = {8fb7bc68903ce7d861c0e4c9110d722b},
keywords = {itc itc28},
month = {Sept},
timestamp = {2020-05-26T16:53:35.000+0200},
title = {Dynamic Virtual Network Traffic Engineering with Energy Efficiency in
Multi-Location Data Center Networks},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Maswood2016.pdf?inline=true},
year = 2016
}