The ask is generic, high traffic and load. First thing would be to have decent understanding of NFR. What kind of application is this? Does the data need to be refreshed with every request? It is content heavy? Where is cloud vs users? With cloud infra, a lot can be achieved using MACH architecture(Cloud you have already stated). Depending on the application, you can cache content /data and decide when to purge the same. This caching can be client side or server side (or network using CDNs as well). You can plan for vertical and horizontal scaling, but infra comes at a cost and you need to understand if cost offsets the benefits of business need. Lastly, do not forget security, there may be tradeoffs on security to achieve speed.

Scaling comes with a cost. Some principles come with cost of manageability or high cloud cost. So, scope of work and goal is important. What works for a 1000 concurrent request may not work for million request and so on.

Start by leveraging robust cloud architecture frameworks like Microsoft Azure's Architecture Framework. Azure's framework provides guidelines, best practices, and principles for designing and managing cloud services and applications. This helps ensure a well-structured and scalable architecture that aligns with key objectives like security, performance, availability, and cost optimization.

Make your application scalable. If we are scaling the application server but bottleneck is database, adding more app server won't help. Well-designed database and queries are prerequisite for high load traffic. Assuming persistent layer is well designed load balancer is the first step.

In my experience few fundamental things needs to be thought through - a. Stateless applications. If applications are stateless, then it is easier to scale horizontally. b. Having thorough understanding of bottlenecks through metrics based approach. Too often a time, we guess and inordinate amount of time is wasted. c. Think through each component, infrastructure, network, code when thinking of scaling vertically/horizontally. d. You are doomed if you overoptimize very early. Event driven approaches, microservices is a good approach for large scale but it has huge cost and use it frivolously at your own peril. e. db can be a factor. eg. thread pool, locking, optimisation of queries, caching can cause issues. @Parsuram's pt is valid

Implement load balancing mechanisms and scaling strategies using Azure Load Balancer and Azure Autoscale. Load balancing efficiently distributes incoming traffic among multiple servers, preventing overloading. Azure Autoscale dynamically adjusts server resources based on demand, allowing for seamless scaling horizontally or vertically to accommodate varying workloads.

While using caching, it is important to decide what to cache, how long to cache. When to update cache and what is the caching strategy. While content delivery system is helpful most of the times. Sometimes, putting data in a map works for the application. Key-value datastore like Amazon DynamoDB which is not a pure caching solution may work for high volume.

Even for API delivery, caching is very important. One of the prickly issue that we solved in Honeywell was that after we profiled our application, we found 5 queries.. really read/select queries was used roughly 1000s of times. The way I saw it was that it was really database driven configuration approach. Caching is one approach / read once and get it into memory is another. Understanding the details is very important.

Utilize caching techniques such as Redis Cache and content delivery solutions like Azure CDN. Caching stores frequently accessed data locally, reducing latency, while content delivery networks distribute data closer to end-users, enhancing speed and reliability. Azure's services facilitate this by optimizing content delivery and minimizing network congestion.

You are doomed if you overoptimize very early. Event driven approaches, microservices is a good approach for large scale but it has huge cost and use it frivolously at your own peril Smaller independent units ie. microservices talking with each other and perhaps going through intermediaries like Kafka means that some time is going to be spent in messaging. This quickly adds up. These are tools, use it judiciously. You may be bleeding/do more damage if you try use of swords to kill ants. Have measured approaches and build testing for performance and scalability early on and not as after thought.

Use Azure services like Azure Kubernetes Service (AKS) and Azure Functions for Microservices and serverless features. Microservices break down applications into smaller, manageable units, while serverless computing, exemplified by Azure Functions, allows for executing code without managing underlying infrastructure. These approaches enable quick scaling and efficient resource utilization in response to fluctuating traffic.

Three things in my view - a. Health of application goes beyond health of infrastructure. At heavy load, what KPIs.. business and technical KPIs are there that we need to understand. How do we know that the applicaiton is doing things alright at heavy load. Particularly when we are migrating to cloud. Matching with legacy helps a lot. Build this as part of DevOps practice. Not only in production, but when testing, developing, these KPIs matter. b. Logging ofcourse requires logging levels that can be turned on/off without shutting down services. Important thing to consider is serviceability. How can you recover if things go wrong. c. Scenario planning: What types of failures are there, what kind of alerts are required and whom to inform.

Implement robust monitoring tools such as Azure Monitor and Azure Log Analytics. These tools collect performance metrics, analyze system behavior, and store event logs, providing insights into the health and performance of the cloud system. Monitoring and logging facilitate real-time tracking, troubleshooting, and optimization to ensure the system operates optimally under high traffic.

Sometimes it is required to think make some services async. For example, the order management software is generally faster than order fulfilment or payment. Depending on whether we are doing it permanently (make fulfilment async) or temporary (make both payment and fulfilment) async. Azure provides Azure message Bus, AWS gives SQS/SES message service, or you can use messaging service available in the cloud.