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- I have cloud infrastructure and don't know its ability to rapidly scale up and down
- I am not sure my infrastructure is secure enough
- I am not sure my application's architecture is adequate to leverage scalable services
- I would like to know how many concurrent user actions my application is able to process
- Optimization of throughput of individual services and components
- On-demand utilization of additional resources to provide enhanced processing capacity when requests loads grow (scale up)
- Release of resources when request loads reduce (scale down)
- Scalability performance measured for cost-optimization
- Operational Excellence, Security, Reliability, Performance Efficiency, Cost, Sustainability analysis
Technical Audit approach
The audit is split into two parallel activities: Architectural analysis and Load testing.
At the initial stage, Architectural analysis is used to build the Performance model. The performance model is then used to predict infrastructure scaling and release conditions. The predicted conditions are translated into a performance test plan.
While the Load tests are running, the architect and load test engineer monitor server load metrics and resource usage to ensure that the system scales as expected. After the peak load is reached, load testing continues for some time to test the behaviour of the system under extreme conditions, and after a while, the load begins to decrease. This is done in order to test that the system returns to its original state without denial of service and performance loss.
Who will perform the Technical Audit
The Cloud architect performs infrastructure setup review and analysis, scalability analysis and performance model development, attends the Performance test plan preparation, and conducts performance test result analysis.
The Performance testing engineer performs Test plan preparation, scenario preparation, test environment configuration, test data generation, test result analysis, test scenario source code packaging, and writes the technical testing report.
Scalability goals examples
- I want elastic infrastructure that scales out automatically based on resource consumption. I need this system to double its capacity so it can handle [1,000] visitors within one hour. The more resources users consume, the more infrastructure I’ll have to provision. On the other hand, if resources are not being consumed, I don’t want to pay for unused infrastructure.
- I want resource utilization to be at no more than 50% of its full capacity, so my components can handle spikes in traffic or situations where capacity is temporarily reduced (i.e. an unhealthy server being replaced or a server being temporarily unavailable during deployments).
- Servers are distributed in at least 2 AWS Availability Zones. Therefore, if one Availability Zone is experiencing problems, I want the application to not suffer from it.
Major benefits of cloud scaling
Fast and Easy – Within a few clicks, you can commission extra VMs to deal with the increasing workload. There is no delay in services as well.
Cost Efficiency – Scaling up/down, in/out, or diagonally is more cost-efficient than setting up an infrastructure that remains underutilized most of the time.
Better Performance – A scalable architecture can deal with sudden increases or decreases in traffic and perform accordingly. There is no wasted resource or lag due to insufficient processing resources.
Capacity – When your business requirements grow, the capacity grows as well. A scalable cloud is, by design, capable of taking care of your growth and increasing data processing requirements.
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