Performance Analysis of Cloud-Assisted Resource Allocation Algorithms in 6G Networks

This paper presents the performance analysis of cloud-assisted resource allocation algorithms in 6G networks. With the rapid growth of wireless applications and network traffic in 6G networks, optimization of resource allocation has become an important challenge for unifying the radio and cloud resources. The cloud-assisted resource allocation algorithms are proposed to enhance the efficiency of resource management and reduce the cost of communication. This paper studies two cloud-assisted resource allocation algorithms: one based on queue-based dynamic resource allocation and the other based on spatial resource partitioning. Both algorithms are evaluated for their performance in terms of latency, throughput, fairness, and latency fairness. The results show that queue-based dynamic resource allocation algorithm performs better than the spatial resource partitioning algorithm in most cases. In addition, this paper presents an analysis of the scalability and robustness of the proposed algorithms. The results show that both algorithms have a good scalability up to tens of users in 6G networks. Finally, this paper proposes a methodology to optimize resource allocation in 6G networks. This proposed framework combines the cloud-assisted resource allocation algorithms with the existing techniques and practices in order to achieve good performance under realistic scenarios.