Analyzing Cloud Performance Optimization: Strategies to Enhance Cold Start Latency

Cold start latency, the delay experienced when starting a new instance of an application in a cloud environment, poses a significant challenge for scalability and agility. This research investigates the factors contributing to cold start latency, including infrastructure limitations, network overhead, and resource provisioning methods. To address cold start latency, various solutions have been proposed and implemented. Traditional approaches such as instance pooling and pre-warming instances offer some benefits but may introduce additional complexity and resource overhead. Containerization technologies have gained popularity due to their lightweight and portable nature, reducing startup overhead. Recent advancements in cloud computing have led to the development of innovative solutions, including predictive scaling and function placement optimization algorithms. These techniques leverage machine learning and predictive analytics to estimate demand and dynamically allocate resources, potentially mitigating cold start latency more effectively. This research contributes to the understanding of cold start latency and explores potential solutions for improving the performance and reliability of cloud-based systems. By addressing cold start latency, organizations can enhance user experience, optimize resource utilization, and improve the overall efficiency of their cloud deployments.