## 5 State-of-the-Art and Open Challenges
### Dynamic vNF Placement  
Most of the current approaches used in vNF placement, presented in Section 4, create a scenario in which all users contribute to a single latency violation threshold, which is common to all users. In line with the long term vision of next generation networks, an improved approach will help providers with different groups of applications such as VoIP/telephony users with a latency violation threshold, which should be lower than the threshold assigned to other classes of applications with less sensitive requirements in terms of latency. One justification that supports this argument is the much-anticipated rise in the number of end users with unique service requirements [57], which will affect the way in which vNFs are deployed in the future.. The placement of virtual Network Functions (vNFs) affects the latency between users and the vNFs, and thus we believe that a better placement design needs to be implemented for future networks, which prioritises the reduction in the negative effects of performance change, caused by the \"hop-by-hop\" movement of users between different vNFs. We argue that the cost implications of placing service functions in the SFC still need to be addressed, which is one of the open challenges in this domain. Other related challenges include creating placement schemes that consider parameters such as subscriber preferences, infrastructure properties, and delivery time. [PERSON] et al. [116] presented a novel fair weighted scheduling (FWS) solution for the scheduling of microservices in multi-cloud environments for the optimal creation of service function chains. Their proposed solution considers delays and SLA-related costs in deploying service function chains. They were able to reduce the overall turnaround time whileconsidering the total network delays and variable loads. The solution was compared to the standard biased greedy approach, which showed a notable increase in performance. The authors acknowledge the need for further investigations into microservices-related challenges such as security, fault tolerance, load balancing, and distributed data management. [PERSON] et al. [117] proposed a parallel placement scheme, PP-DRL, which uses deep reinforcement learning (DRL) to deploy SFCs optimally with minimal resource costs. They also used DRL to determine the right servers that could host service functions by collecting the characteristics of user requests as state information. They first used DRL to calculate the number of virtual machines and to find servers that can be used for hosting vNFs. The location of the end users is not prioritised in the scheme. In their work, [PERSON] et al. [97] presented an efficient tool, which they called the \"Network Slice Planner\", for spatio-temporal simulation of mobile service usage, to maximise QoS for the users. They modified the classic predictive algorithm after presenting a set of existing placement algorithms. The performance of the enhanced predictive algorithm was compared with existing vNF placement algorithms, which showed slightly better results. Although they modelled the behaviour of the end users (in terms of traffic types), their proposed solution does not prioritise the placement of vNFs at the edge of the network. [PERSON] et al. [11] explain how the optimal placement of vNFs across multiple clouds is a problem which, when solved, can help in the optimization of parameters such as cost, network delay, and bandwidth. After presenting the components of an SFC environment, they presented an analytical model for the placement of service functions in multi-cloud network environments. Their work considers delays to end-users, QoS, and SLA. They employed an ILP approach to obtain the optimal solution, which was achieved by setting up an objective function and applicable constraints. [PERSON] et al. [118] considered the placement of vNFs in a distributed-edge NFV environment with dynamic orchestration and re-calculation of vNF placement.