Evaluating the impact of delay constraints in network services for intelligent network slicing based on SKM model

Solving the problem of network resource allocation with delay constraint is a significant challenge for realizing future Internet and 5G networks services such as advanced mobile broadband services and Internet of things (IoT), especially under the network slicing scenario. The impact of delay constraints may lead to rejection of demands, resulting in low resource utilization of network resources. This is especially severe when dynamic traffic is considered. Therefore, intelligent resource allocation algorithms are required to use the network resources in delay constrained scenario efficiently. Moreover, these algorithms should guarantee quality of service (QoS) between different priority slices during congestion case. Therefore, in this paper, we analyze the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient squatting and kicking model (SKM), proved in other works to be the most effective up to the present time yet. SKM incorporates kicking and squatting of resources as innovative techniques enabling it to achieve 100% resource utilization and acceptance ratio for higher priority slices in scenarios where the other state of art algorithms not able to reach by far in some scenarios. Simulation results showed that incorporating delay constraints has a significant impact on the performance, resulting in up to 10% and 4% reduction in terms of average resource utilization and acceptance ratios respectively.