

The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues’ parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
| Engineering controlled terms: | Application programsBenchmarkingComplex networksQuality of serviceQueueing networksQueueing theorySoftware defined networking |
|---|---|
| Engineering uncontrolled terms | 5g/6gAcademic communityDynamic QoS managementKey performance indicatorsNetwork coreNetwork slicingProfessional communityProgrammabilityQueue managementSoftware-defined networkings |
| Engineering main heading: | 5G mobile communication systems |
| EMTREE medical terms: | artificial intelligencesoftware |
| MeSH: | Artificial IntelligenceSoftware |
| Funding sponsor | Funding number | Acronym |
|---|---|---|
| 101016499 | ||
| Horizon 2020 Framework Programme See opportunities by H2020 | 856967,101016499 | H2020 |
| UK Research and Innovation | 134030 | UKRI |
Funding: This work was supported by the European Union\u2019s Horizon 2020 research and innovation program under Grant Agreement number 856967, and by the H2020 European Union\u2019s Horizon 2020 research and innovation program under Grant Agreement number 101016499.
Bojović, P.D.; The School of Computing, Union University Belgrade, 6/6 Knez Mihailova, Belgrade, Serbia;
© Copyright 2022 Elsevier B.V., All rights reserved.