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Data uncertainty in virtual network embedding: robust optimization and protection levels

Data uncertainty in virtual network embedding: robust optimization and protection levels
Data uncertainty in virtual network embedding: robust optimization and protection levels
We address the virtual network embedding problem (VNE) which, given a physical (substrate) network and a collection of virtual networks (VNs), calls for an embedding of the most profitable subset of VNs onto the physical substrate, subject to capacity constraints. In practical applications, node and link demands of the different VNs are, typically, uncertain and difficult to know a priori. To face this issue, we first model VNE as a chance-constrained Mixed-Integer Linear Program (MILP) where the uncertain demands are assumed to be random variables. We then propose a ΓΓ -robust optimization approach to approximate the original chance-constrained formulation, capable of yielding solutions with a large profit that are feasible for almost all the possible realizations of the uncertain demands. To solve larger scale instances, for which the exact approach is computationally too demanding, we propose two MILP-based heuristics: a parametric one, which relies on a parameter setting chosen a priori, and an adaptive one, which does not. We conclude by reporting on extensive computational experiments where the different methods and approaches are compared.
1064-7570
681-710
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Koster, Arie
22c70cb3-4f20-4721-9694-1a45a623c2f8
Tieves, Martin
dfba8a3e-6f1a-46fb-a501-d58cccd6dac1
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Koster, Arie
22c70cb3-4f20-4721-9694-1a45a623c2f8
Tieves, Martin
dfba8a3e-6f1a-46fb-a501-d58cccd6dac1

Coniglio, Stefano, Koster, Arie and Tieves, Martin (2016) Data uncertainty in virtual network embedding: robust optimization and protection levels. Journal of Network and Systems Management, 24 (3), 681-710. (doi:10.1007/s10922-016-9376-x).

Record type: Article

Abstract

We address the virtual network embedding problem (VNE) which, given a physical (substrate) network and a collection of virtual networks (VNs), calls for an embedding of the most profitable subset of VNs onto the physical substrate, subject to capacity constraints. In practical applications, node and link demands of the different VNs are, typically, uncertain and difficult to know a priori. To face this issue, we first model VNE as a chance-constrained Mixed-Integer Linear Program (MILP) where the uncertain demands are assumed to be random variables. We then propose a ΓΓ -robust optimization approach to approximate the original chance-constrained formulation, capable of yielding solutions with a large profit that are feasible for almost all the possible realizations of the uncertain demands. To solve larger scale instances, for which the exact approach is computationally too demanding, we propose two MILP-based heuristics: a parametric one, which relies on a parameter setting chosen a priori, and an adaptive one, which does not. We conclude by reporting on extensive computational experiments where the different methods and approaches are compared.

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Accepted/In Press date: 27 March 2016
e-pub ahead of print date: 28 May 2016
Published date: 28 May 2016
Organisations: Operational Research

Identifiers

Local EPrints ID: 394031
URI: http://eprints.soton.ac.uk/id/eprint/394031
ISSN: 1064-7570
PURE UUID: 0c9a4766-7189-4d98-b8ac-346c37e288ba
ORCID for Stefano Coniglio: ORCID iD orcid.org/0000-0001-9568-4385

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Date deposited: 10 May 2016 13:25
Last modified: 15 Mar 2024 05:34

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Contributors

Author: Arie Koster
Author: Martin Tieves

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