Architectural performance efficiency focuses on efficiently using application infrastructure and resources to meet increasing demand and technology evaluation. Technology vendors and open source communities continuously work to improve the performance of applications. Often, large enterprises continue to work on legacy programming languages and technologies because of fear of changing and taking risks. As technology evolves, it often addresses critical performance issues, and the advancement of technology in your application helps to improve application performance.

Many large public cloud providers, such as Amazon Web Service (AWS), Microsoft Azure, and Google Cloud Platform (GCP), offer technology as a service. This makes it easier to adopt enterprise jobs and complex technologies more efficiently and with minimal effort—for example, you might use storage as a service to manage a massive amount of data or a NoSQL database as a managed service to provide high-performance scalability to your application.

Now organizations can utilize a content distribution network (CDN) to store heavy image and video data near user locations to reduce network latency and improve performance. With infrastructure as a service (IaaS), it becomes easier to deploy workloads closer to your user base, which helps to optimize application performance by reducing latency over the network.

As servers virtualize, you can be more agile and experiment with your application, and you can apply a high degree of automation. Agility helps you to experiment and find out what technology and method are best suited for your application workload—for example, my server deployment should go for virtual machines or containers, or it can even go completely serverless with the use of AWS Lambda, which is a Function as a Service (FaaS). Let's look at some vital design principles to consider for your workload performance optimization.