Cloud computing is now a modern model for managing, configuring, and accessing distributed computing systems around the network on a full scale. One of cloud computing's fundamental problems is the balancing of loads, which is essential for evenly distributing the workload across all nodes. Over the years, scholars have proposed various approaches to resolve this problem. Nevertheless, optimizations of task execution time, completion time, response time, and virtual machine resources (VMs) are still posing tremendous challenges. This study proposes a new load balancing algorithm, which combines maximum-minimum and round-robin algorithm (MMRR) so that tasks with long execution time are allocate using maximum-minimum and tasks with lowest execution task will be assigned using round-robin. Cloud analyst tool software was used to introduce the new load balancing techniques, and a comparative analysis with the existing algorithm was conducted to optimize cloud services to clients. The study findings indicate that Maximum Minimum Round Robin (MMRR) has brought significant changes to cloud services. The data centre's loading time is good from both Throttled and MMRR, but Round Robin was worst. MMRR performed better from the algorithms tested based on the whole response time and cost-effectiveness (89%). The study suggested that MMRR should be implemented for enhancing user satisfaction in the cloud service.

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient’s blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models.