Application SLA

Azure customers can use SLAs to evaluate how their Azure solutions meet their business requirements and the needs of their clients and users. By creating your own SLAs, you can set performance targets to suit your specific Azure application. When creating an Application SLA consider the following:

Identify workloads. A workload is a distinct capability or task that is logically separated from other tasks, in terms of business logic and data storage requirements. Each workload has different requirements for availability, scalability, data consistency, and disaster recovery. To ensure that application architecture meets your business requirements, define target SLAs for each workload. Account for the cost and complexity of meeting availability requirements, in addition to application dependencies.
Plan for usage patterns. Usage patterns also play a role in requirements. Identify differences in requirements during critical and non-critical periods. For example, a tax-filing application can't fail during a filing deadline. To ensure uptime, plan redundancy across several regions in case one fails. Conversely, to minimize costs during non-critical periods, you can run your application in a single region.
Establish availability metrics — mean time to recovery (MTTR) and mean time between failures (MTBF). MTTR is the average time it takes to restore a component after a failure. MTBF is how long a component can reasonably expect to last between outages. Use these measures to determine where to add redundancy and to determine service-level agreements (SLAs) for customers.
Establish recovery metrics — recovery time objective and recovery point objective (RPO). RTO is the maximum acceptable time an application can be unavailable after an incident. RPO is the maximum duration of data loss that is acceptable during a disaster. To derive these values, conduct a risk assessment and make sure you understand the cost and risk of downtime or data loss in your organization.
Implement resiliency strategies. Resiliency is the ability of a system to recover from failures and continue to function. Implement resiliency design patterns, such as isolating critical resources, using compensating transactions, and performing asynchronous operations whenever possible.
Build availability requirements into your design. Availability is the proportion of time your system is functional and working. Take steps to ensure that application availability conforms to your service-level agreement. For example, avoid single points of failure, decompose workloads by service-level objective, and throttle high-volume users.

Understand your app requirements

Building an efficient and reliable Azure solution requires knowing your workload requirements. You can then select Azure products and services, and provision resources according to those requirements. It's important to understand the Azure SLAs that define performance targets for the Azure products and services within your solution. This understanding will help you create achievable Application SLAs.

In a distributed system, failures will happen. Hardware can fail. The network can have transient failures. It's rare for an entire service or region to experience a disruption, but even this must be planned for.

Resiliency

Resiliency is the ability of a system to recover from failures and continue to function. It's not about avoiding failures, but responding to failures in a way that avoids downtime or data loss. The goal of resiliency is to return the application to a fully functioning state following a failure. High availability and disaster recovery are two crucial components of resiliency.

When designing your architecture you need to design for resiliency, and you should perform a Failure Mode Analysis (FMA). The goal of an FMA is to identify possible points of failure and to define how the application will respond to those failures.

Cost and complexity vs. high availability

Availability refers to the time that a system is functional and working. Maximizing availability requires implementing measures to prevent possible service failures. However, devising preventative measures can be difficult and expensive, and often results in complex solutions.

As your solution grows in complexity, you will have more services depending on each other. Therefore, you might overlook possible failure points in your solution if you have several interdependent services.

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For example: A workload that requires 99.99 percent uptime shouldn't depend upon a service with a 99.9 percent SLA.

Most providers prefer to maximize the availability of their Azure solutions by minimizing downtime. However, as you increase availability, you also increase the cost and complexity of your solution.

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For example: An SLA that defines an uptime of 99.999% only allows for about 5 minutes of total downtime per year.

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.

✔️ Performance targets about 99.99% are going to be very difficult to achieve.

	Creado por Cristian Osvaldo Gómez hace más de 4 años

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