We study the renting servers in the cloud problem (RSiC), inspired by job allocation in cloud computing environments. In this problem, jobs arrive sequentially in an online manner, and their sizes are revealed upon arrival. If a job’s duration is also known at arrival, the scenario is classified as clairvoyant; otherwise, it is non-clairvoyant. Each job must be assigned to a server, with servers available on-demand and subject to a fixed capacity per unit of time. The number of servers that can be rented is unlimited, and the objective is to minimize the total rental time of all servers. In this paper, we evaluate the performance of nearly all existing clairvoyant and non-clairvoyant algorithms for the RSiC problem. Additionally, we introduce new algorithms, which were derived from combinations of existing algorithms. Some of the introduced algorithms outperform all previously known algorithms in our experimental evaluations. Unlike prior studies that exclusively utilized synthetic data, we use real-world Azure data from [4] in our experiments. This dataset captures large-scale virtual machine (VM) allocation across Azure’s availability zones, offering a more practical perspective on the operational effectiveness of these algorithms.