Cloud concepts to consider and underrstand for the Microsoft Az-900 Exam
Benefits and Considerations of using Cloud Services
- High Availability
- Fault tolerance
Economies of scale
Capital Expenditure (CapEx) vs Operational Expenditure (OpEx)
The Consumption-Based Model
The time that a system is functional and working is Availability. In order to maximize this availability, you need to implement preventative measure against failure. Keep in my mind, implementations of these preventative measures can be difficult and costly (money-wise). This often results in complex solutions.
As your solution grows in complexity, you will have more services dependant on one another. Therefore, you might overlook possible failure points in your solution if you have several interdependent services.
For example: A workload that requires 99.99 percent uptime shouldn’t depend on a service with a 99.9 percent SLA.
Most providers prefer to maximize the availability of their Azure solutions by minimizing downtime. Again; however, as you increase availability, you also increase the cost and complexity of your solution.
For example: an SLA that defines an uptime of 99.99% only allows for about 5 minutes of total downtime per month. –you pay for uptime.
The risk of potential downtime is cumulative across various SLA levels, which means that complex solutions can face greater availability challenges. Therefore, how critical high-availability is to your requirements will determine how you handle the addition of complexity and cost to your application SLAs.
Considerations for defining Application SLAs
- If your application SLA defines four 9’s (99.99%) performance targets, recovering from failures by manual intervention may not be enough to fulfill your SLA. Your Azure solution must be self-diagnosing and self-healing instead.
- It is difficult to respond to failures quickly enough to meet SLA performance targets above four 9’s.
- Carefully consider the time window against which your application SLA performance targets are measured. The smaller the time window, the tighter the tolerances. If you define your application SLA as hourly or daily uptime, you need to understand these tighter tolerances might not allow for achievable performance targets.
scaling up and scaling out
Public facing applications might grow rapidly or an internal application might need to support a larger user base as the business grows. Even when we predict load, it’s rarely flat. We’ll define scaling up/down and scaling out/in, cover ways Azure can improve scaling capabilities, and look at how serverless and container technologies can improve your architecture’s ability to scale.