We develop a partial sharing model for providers described as a multi
server system. This is a generalisation of the full pooling that
have been studied in the literature. Partial sharing allows
providers to possibly pool a fraction of their resources when full
pooling is not beneficial to them. Two M/M/N queues with redundant
service models are considered. Copies of an arriving job are placed
in the queues of servers that can serve the job. Partial sharing
models for cancel-on-complete and cancel-on-start redundancy models
are developed. For cancel-on-complete, it is shown that the Pareto
efficient region is the full pooling configuration. For a
cancel-on-start policy, we conjecture that the Pareto frontier is
always non-empty and is such that at least one of the two providers
is sharing all of its resources. For this system, using bargaining
theory the sharing configuration that the providers may use is
determined. Mean response time and probability of waiting are the
performance metrics considered.
%0 Conference Paper
%1 met19ITC31
%A Mete, Akshay
%A Manjunath, D.
%A Nair, Jayakrishnan
%A Prabhu, Balakrishna
%B 31th International Teletraffic Congress (ITC 31)
%C Budapest, Hungary
%D 2019
%K itc itc31
%T Partial Server Pooling in Redundancy Systems
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/met19ITC31.pdf?inline=true
%X We develop a partial sharing model for providers described as a multi
server system. This is a generalisation of the full pooling that
have been studied in the literature. Partial sharing allows
providers to possibly pool a fraction of their resources when full
pooling is not beneficial to them. Two M/M/N queues with redundant
service models are considered. Copies of an arriving job are placed
in the queues of servers that can serve the job. Partial sharing
models for cancel-on-complete and cancel-on-start redundancy models
are developed. For cancel-on-complete, it is shown that the Pareto
efficient region is the full pooling configuration. For a
cancel-on-start policy, we conjecture that the Pareto frontier is
always non-empty and is such that at least one of the two providers
is sharing all of its resources. For this system, using bargaining
theory the sharing configuration that the providers may use is
determined. Mean response time and probability of waiting are the
performance metrics considered.
@inproceedings{met19ITC31,
abstract = {We develop a partial sharing model for providers described as a multi
server system. This is a generalisation of the full pooling that
have been studied in the literature. Partial sharing allows
providers to possibly pool a fraction of their resources when full
pooling is not beneficial to them. Two M/M/N queues with redundant
service models are considered. Copies of an arriving job are placed
in the queues of servers that can serve the job. Partial sharing
models for cancel-on-complete and cancel-on-start redundancy models
are developed. For cancel-on-complete, it is shown that the Pareto
efficient region is the full pooling configuration. For a
cancel-on-start policy, we conjecture that the Pareto frontier is
always non-empty and is such that at least one of the two providers
is sharing all of its resources. For this system, using bargaining
theory the sharing configuration that the providers may use is
determined. Mean response time and probability of waiting are the
performance metrics considered.},
added-at = {2020-04-29T15:29:04.000+0200},
address = {Budapest, Hungary},
author = {Mete, Akshay and Manjunath, D. and Nair, Jayakrishnan and Prabhu, Balakrishna},
biburl = {https://www.bibsonomy.org/bibtex/22630e71f30ee30cd3ece5a2e4c038e33/itc},
booktitle = {31th International Teletraffic Congress (ITC 31)},
interhash = {d93b2dc4ef82a4957f1992a82e5303ec},
intrahash = {2630e71f30ee30cd3ece5a2e4c038e33},
keywords = {itc itc31},
timestamp = {2020-04-30T18:18:45.000+0200},
title = {Partial Server Pooling in Redundancy Systems},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/met19ITC31.pdf?inline=true},
year = 2019
}