A Probabilistic Optimization Approach to the Equitable Controller Location Problem

• Published in: IEEE eTransactions on network and service management Vol. 22; no. 2; pp. 1812 - 1824
• Main Authors: Ballasheni, Erison, Nace, Dritan, Tomaszewski, Artur, Zyle, Alban
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer Science; Control systems; controller placement; Controllers; Costs; Cybersecurity; Denial-of-service attack; Integer programming; Linear programming; Load modeling; Mathematical models; Mathematics; Mixed integer; network resilience; node attacks; Numerical models; Optimization; Placement; Probabilistic logic; Resilience; SDN; Software-defined networking; solution fairness; Switches; Uncertainty; solution fairness; network resilience; SDN; Controller placement; node attacks
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2024.3500095

The ability of the Software-Defined Network (SDN) to transport traffic flows depends, in particular, on the SDN switches being able to communicate with SDN controllers, which are responsible for the setup of network connections and the configuration of switches. Since in principle the number of SDN controllers is limited they must be installed in a set of carefully selected node locations. Whereas the problem of controller placement is well defined and has been thoroughly studied and effectively solved for the nominal network state, it becomes difficult when the network is subject to attacks, especially as they can occur anywhere and anytime. In this paper we tackle the problem of controller placement resilient to network attacks considering probabilistic characterisation of attacks and equitable access of switches to controllers. We treat the problem as a specific facility location problem with the objective of providing resilience to network attacks, and analyze a couple of major solution fairness criteria. We derive a number of Mixed-Integer Linear Programming (MILP) problem models that exploit robust optimization and fair optimization concepts and techniques, and examine their effectiveness by means of a numerical study that uses five transport networks.