Data processing tasks are increasingly spread across the internet to account for the spatially distributed nature of many data sources. In order to use network resources efficiently, subtasks need to be distributed in the network so data can be filtered close to the data sources. Previous approaches to this operator placement problem relied on various heuristics to constrain the complexity of the problem. In this paper, we propose two generic integer constrained problem formulations: a topology aware version which provides a placement including the specific network links as well as an end-to-end delay aware version which relies on the routing capabilities of the network. A linear programming relaxation for both versions is provided which allows exact and efficient solution using common solvers.
%0 Conference Paper
%1 carabelli2012exact
%A Carabelli, Ben W.
%A Benzing, Andreas
%A Dürr, Frank
%A Koldehofe, Boris
%A Rothermel, Kurt
%A Seyboth, Georg S.
%A Blind, Rainer
%A Bürger, Mathias
%A Allgöwer, Frank
%B Proceedings of the 51st IEEE Conference Decision and Control (CDC)
%D 2012
%I IEEE
%K mult sent ubs_10005 ubs_10007 ubs_20008 ubs_20011 ubs_30082 ubs_30113 ubs_40118 ubs_40173 unibibliografie
%P 3777-3782
%R 10.1109/CDC.2012.6426790
%T Exact convex formulations of network-oriented optimal operator placement
%X Data processing tasks are increasingly spread across the internet to account for the spatially distributed nature of many data sources. In order to use network resources efficiently, subtasks need to be distributed in the network so data can be filtered close to the data sources. Previous approaches to this operator placement problem relied on various heuristics to constrain the complexity of the problem. In this paper, we propose two generic integer constrained problem formulations: a topology aware version which provides a placement including the specific network links as well as an end-to-end delay aware version which relies on the routing capabilities of the network. A linear programming relaxation for both versions is provided which allows exact and efficient solution using common solvers.
%@ 978-1-4673-2066-5 and 978-1-4673-2065-8 and 978-1-4673-2063-4 and 978-1-4673-2064-1
@inproceedings{carabelli2012exact,
abstract = {Data processing tasks are increasingly spread across the internet to account for the spatially distributed nature of many data sources. In order to use network resources efficiently, subtasks need to be distributed in the network so data can be filtered close to the data sources. Previous approaches to this operator placement problem relied on various heuristics to constrain the complexity of the problem. In this paper, we propose two generic integer constrained problem formulations: a topology aware version which provides a placement including the specific network links as well as an end-to-end delay aware version which relies on the routing capabilities of the network. A linear programming relaxation for both versions is provided which allows exact and efficient solution using common solvers.},
added-at = {2019-12-16T13:04:10.000+0100},
author = {Carabelli, Ben W. and Benzing, Andreas and Dürr, Frank and Koldehofe, Boris and Rothermel, Kurt and Seyboth, Georg S. and Blind, Rainer and Bürger, Mathias and Allgöwer, Frank},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/286ad64138f69bf5a9714e4ca7ffd0046/unibiblio},
booktitle = {Proceedings of the 51st IEEE Conference Decision and Control (CDC)},
doi = {10.1109/CDC.2012.6426790},
eventdate = {2012-12-10/2012-12-13},
eventtitle = {51st IEEE Conference Decision and Control 2012 (CDC)},
interhash = {82995c9e951dfcbc0e13581c43718325},
intrahash = {86ad64138f69bf5a9714e4ca7ffd0046},
isbn = {{978-1-4673-2066-5} and {978-1-4673-2065-8} and {978-1-4673-2063-4} and {978-1-4673-2064-1}},
keywords = {mult sent ubs_10005 ubs_10007 ubs_20008 ubs_20011 ubs_30082 ubs_30113 ubs_40118 ubs_40173 unibibliografie},
language = {eng},
pages = {3777-3782},
publisher = {IEEE},
timestamp = {2019-12-16T12:04:10.000+0100},
title = {Exact convex formulations of network-oriented optimal operator placement},
venue = {Maui, HI},
year = 2012
}
