Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

Abstract

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

Keywords

• 5G
• radio access network
• network slices
• Cloud-RAN slicing
• dynamic resource allocation
• slice admission control