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In recent years, the demand for computing resources in daily life has risen due to the rapid development of smart terminal devices. Cloud computing facilities may not be able to handle the massive amounts of data that will be generated in the future. Edge computing, a new paradigm after cloud computing, places edge servers closer to the terminals, improving the efficiency of processing the massive data generated by the terminals. However, during actual use, the edge computing system is limited by factors such as population density and service demand, resulting in suboptimal data processing efficiency and operator profits. This paper studies the deployment of edge servers in a fixed area, focusing on operator profitability. We propose the Advanced K-Means method, an improvement of K-means for users with different needs in a fixed area, to maximize system profitability at various times. Experimental results based on subscriber and base station datasets from Melbourne’s Central Business District (CBD) demonstrate that this deployment strategy improves profitability by at least 22 % , outperforming several other representative approaches. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) offers cloud computing capabilities and IT services situated at the Radio Access Network (RAN) in the mobile users' proximity. Applications could offload their computation-intensive tasks to the MEC servers. Consequently, MEC significantly diminishes the mean response time and job rejection probability compared to conventional Mobile Cloud Computing (MCC). Cost-performance trade-off is one of the major concerns of the system designers. Low performance leads to the Service Level Agreement (SLA) violation and disappoints the service consumers. On the other hand, reaching high performance by augmenting the number of servers in the MEC and Cloud sides incur more infrastructure and other operational costs. In this paper, we formulate the mentioned cost-performance trade-off into an optimization problem. We demonstrate that the optimization problem is integer and non-linear. Moreover, we propose a capacity planning framework to determine the optimal number of servers in the MEC and Cloud sides, minimizing the Total Cost of Ownership (TCO) with SLA satisfaction. The proposed capacity planning framework gains from the simulated annealing algorithm to obtain a globally optimum solution. Furthermore, we deploy the stochastic performance model to measure mean response time and job rejection probability at each iteration. Numerical results reveal that the proposed framework determines the optimal solution within a reasonable time. [ABSTRACT FROM AUTHOR]