Multi-Objective genetic algorithm for fast service function chain reconfigurationShow others and affiliations
2023 (English)In: IEEE Transactions on Network and Service Management, E-ISSN 1932-4537, Vol. 20, no 3, p. 3501-3522Article in journal (Refereed) Published
Abstract [en]
The optimal placement of virtual network functions (VNFs) improves the overall performance of servicefunction chains (SFCs) and decreases the operational costs formobile network operators. To cope with changes in demands,VNF instances may be added or removed dynamically, resourceallocations may be adjusted, and servers may be consolidated.To maintain an optimal placement of SFCs when conditionschange, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, suchreconfigurations may lead to stress on the VNF infrastructure,which may cause service degradation. On the other hand, notchanging the placement may lead to suboptimal operation,and servers and links may become congested or underutilized,leading to high operational costs. In this paper, we investigatethe trade-off between the reconfiguration of SFCs and theoptimality of their new placement and service-flow routing. Wedevelop a multi-objective genetic algorithm that explores thePareto front by balancing the optimality of the new placementand the cost to achieve it. Our numerical evaluations show thata small number of reconfigurations can significantly reduce theoperational cost of the VNF infrastructure. In contrast, toomuch reconfiguration may not pay off due to high costs. Webelieve that our work provides an important tool that helpsnetwork providers to plan a good reconfiguration strategy fortheir service chains.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 20, no 3, p. 3501-3522
Keywords [en]
Cloud computing, Containers, Cost engineering, Transfer functions, Cloud-computing, Migration strategy, Multi-objectives genetic algorithms, Network functions, Networks reconfiguration, Optimisations, Resource management; Virtual network function, Virtual networks, VNF migration strategy, Genetic algorithms
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-91583DOI: 10.1109/TNSM.2022.3195820ISI: 001142524900006Scopus ID: 2-s2.0-85135744199OAI: oai:DiVA.org:kau-91583DiVA, id: diva2:1689908
Funder
Knowledge Foundation2022-08-242022-08-242024-07-04Bibliographically approved