Abstract: Cloud-based applications have gained significant traction in recent years due to their scalability and flexibility. However, ensuring optimal performance for such applications remains a challenge. This research paper proposes a novel algorithm aimed at enhancing the performance of cloud-based virtualized microservice applications using deep Q learning. The algorithm focuses on optimizing various aspects of the application, including resource allocation on virtual machines, load balancing, and credit. It leverages deep learning techniques to dynamically adjust resource allocation based on workload patterns and performance metrics. By intelligently distributing the workload across virtualized microservices, the algorithm aims to minimize response times and maximize resource utilization by applying concepts of deep learning. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on a realistic cloud-based virtual machine based microservice environment. Performance metrics such as response time, throughput, and resource utilization are measured and compared with a deep learning approach. The results demonstrate that the proposed algorithm significantly improves the performance of application in cloud environment. It achieves reduced response times, increased throughput, and better resource utilization compared to traditional load balancing techniques. Furthermore, the algorithm adapts to changing workloads and effectively manages resources, ensuring optimal performance even under varying conditions.

Key words: CPU utilization, cloud computing, load balancing, deep Q learning, microservices