Server-less computing is a novel cloud-based paradigm that is gaining popularity today for running widely distributed applications. When it comes to server-less computing, features are available via subscription. Server-less computing is advantageous to developers since it lets them install and run programs without worrying about the underlying architecture. A common choice for code deployment these days, server-less design is preferred because of its independence, affordability, and simplicity. The healthcare industry is one excellent setting in which server-less computing can shine. In the existing literature, we can see that fewer studies have been put forward or explored in the area of server-less computing with respect to smart healthcare systems. A cloud infrastructure can help deliver services to both users and healthcare providers. The main aim of our research is to cover various topics on the implementation of server-less computing in the current healthcare sector. We have carried out our studies, which are adopted in the healthcare domain and reported on an in-depth analysis in this article. We have listed various issues and challenges, and various recommendations to adopt server-less computing in the healthcare sector.

Nowadays, real-world applications are primarily concerned with collecting the physical characteristics that correspond to various geographical phenomena, and information plays a critical role in the analysis and forecast of various occurrences that occur. Real-time analysis is used in a variety of application domains, including traffic flow monitoring, healthcare monitoring, and so on. This research study has considered the existing geospatial serverless computing framework and proposed an analytical model to enable the geospatial serverless platform to compute and operate at different workloads and the tradeoff between the cost and performance are then determined by the users’ preferences. Here, the accuracy of the model is validated in Amazon Web Service (AWS) Lambda environment and the model calculations are shown based on the performance metrics by including average response time, probability of cold start as well as the average amount of functional instances in the steady state by using the queuing theory. Here, the performance modelling is carried out in the geospatial serverless environment for different workloads, which results in delivering better performance at a minimal cost.