# QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities
## 4 Conclusions  
The findings presented in this work touch upon topics that have been certainly discussed with relation to 5G resilience [27]. Our contribution relies in the direct calculation of delay as a sum of two discrete components based on different conditions during the transmission of TCP/IP packets, a novel feature in the literature. The applicability of this innovative scheme ranges in many of the current 5G paradigms such as smart health systems [28], services oriented towards smart cities [29], the implementation of remote digital control for Industry 4.0 [30], and the Internet-of-Ships [31]. These use cases require specific constraints in terms of average packet delay as per URLLC [32], while demanding cloud-based architectures [33].  
The strict preservation of URLLC requirements as well as the restriction (upper-bound) of transmission delay, even when URLLC requirements do not apply, is a fundamental pillar of QoS [34; 35; 36; 37; 38], as per ITU specifications [39; 40; 41]. The satisfaction of such QoS provisions is not merely desired from a technical standpoint, but also from the perspective of user experience, as show in [42], assessing the QoE on the basis of 5G-enabled broadband services with an upper-bound on latency.  
All such provisions and requirements are certain to be even stricter within the context of Beyond-5G/6G designation [43], with the shift towards higher spectrum (i.e., mmW bands) and therefore more available bandwidth for ultra-wideband beamforming and higher-order modulation [44]. Therefore, our contribution aspires to address challenges not only within present-day microwave-driven 5G, but also for the upcoming challenges of the mm-wave/THz and beyond bands.  
Some future extension to our contribution might be the comparison of our QoS approach with the proposed general methodology and model in the excellent book chapter in [45]. This might be useful in order to evaluate different QoSs and queue metrics and try to enhance the overall modeling. Moreover, in addition to our static analysis of pre-configured QoS settings in the network, a dynamic QoS configuration could be another interesting approach, especially in the evolving cloud-based RAN architectures. This dynamic QoS configuration has been proposed in [45] through dynamic queue management that implies the ability to dynamically change queues' parameter configurations according to the requirements specified through the slice orchestration on the application layer. However, what is missing is the analysis of different queues on different parts of the network infrastructure, as has been proposed and tackled in our contribution.  
Conceptualization, S.L. and M.P.; methodology, S.L., M.P. and T.C.; validation, M.P. and T.C.; formal analysis, S.L., M.P. and T.C.; investigation, S.L., M.P. and T.C.; resources, M.P. and T.C.; data curation, M.P. and T.C.; writing--original draft preparation, S.L.; writing--review and editing, M.P. and T.C.; visualization, M.P. and T.C.; supervision, S.L. and M.P.; project administration, T.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Institutional Review Board Statement: Excluded. Informed Consent Statement: Not relevant. Data Availability Statement: Not relevant. Conflicts of Interest: The authors declare no conflict of interest.