Optimization-Based Resource Management Strategies for 5G C-RAN Slicing Capabilities

In an emerging paradigm in 5G networks, the computations of various types of mobile applications are offloaded to cloud environments. Edge and core clouds provide computing, storage, and networking resources to serve as a generic computing platform. Network slicing techniques offer an effective way to boost various types of services, such as delay-sensitive or computationally intensive application, that are deployed on-demand in a shared resource infrastructure. This study proposes a resource management mechanism to optimize the quality of experience (QoE) of users in terms of the delay gap tolerance. The Min-Fit algorithm is a heuristic-based solution that flexibly chooses a server that has the maximum remaining CPU resources for satisfying user requirements. A mathematical model is formulated for 5G slicing capabilities to optimize the delay gap using a resource management approach to achieve QoE. Some computational experiments are demonstrated as performance evaluations for verifying the suitability of our proposed approach in terms of slicing capabilities. The results show that our approach outperforms other algorithms, which have larger delay gaps.