Throttling Pattern

Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service. This can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.

Context and problem

The load on a cloud application typically varies over time based on the number of active users or the types of activities they are performing. For example, more users are likely to be active during business hours, or the system might be required to perform computationally expensive analytics at the end of each month. There might also be sudden and unanticipated bursts in activity. If the processing requirements of the system exceed the capacity of the resources that are available, it’ll suffer from poor performance and can even fail. If the system has to meet an agreed level of service, such failure could be unacceptable.

There’re many strategies available for handling varying load in the cloud, depending on the business goals for the application. One strategy is to use autoscaling to match the provisioned resources to the user needs at any given time. This has the potential to consistently meet user demand, while optimizing running costs. However, while autoscaling can trigger the provisioning of additional resources, this provisioning isn’t immediate. If demand grows quickly, there can be a window of time where there’s a resource deficit.

When to use this pattern

Use this pattern:

  • To ensure that a system continues to meet service level agreements.
  • To prevent a single tenant from monopolizing the resources provided by an application.
  • To handle bursts in activity.
  • To help cost-optimize a system by limiting the maximum resource levels needed to keep it functioning.

Solution

An alternative strategy to autoscaling is to allow applications to use resources only up to a limit, and then throttle them when this limit is reached. The system should monitor how it’s using resources so that, when usage exceeds the threshold, it can throttle requests from one or more users. This will enable the system to continue functioning and meet any service level agreements (SLAs) that are in place

The system could implement several throttling strategies, including:

  • Rejecting requests from an individual user who’s already accessed system APIs more than n times per second over a given period of time. This requires the system to meter the use of resources for each tenant or user running an application. For more information, see the Service Metering Guidance.
  • Disabling or degrading the functionality of selected nonessential services so that essential services can run unimpeded with sufficient resources. For example, if the application is streaming video output, it could switch to a lower resolution.
  • Using load leveling to smooth the volume of activity (this approach is covered in more detail by the Queue-based Load Leveling pattern). In a multi-tenant environment, this approach will reduce the performance for every tenant. If the system must support a mix of tenants with different SLAs, the work for high-value tenants might be performed immediately. Requests for other tenants can be held back, and handled when the backlog has eased. The Priority Queue pattern could be used to help implement this approach.
  • Deferring operations being performed on behalf of lower priority applications or tenants. These operations can be suspended or limited, with an exception generated to inform the tenant that the system is busy and that the operation should be retried later.

Issues and considerations

You should consider the following points when deciding how to implement this pattern:

  • Throttling an application, and the strategy to use, is an architectural decision that impacts the entire design of a system. Throttling should be considered early in the application design process because it isn’t easy to add once a system has been implemented.
  • Throttling must be performed quickly. The system must be capable of detecting an increase in activity and react accordingly. The system must also be able to revert to its original state quickly after the load has eased. This requires that the appropriate performance data is continually captured and monitored.
  • If a service needs to temporarily deny a user request, it should return a specific error code so the client application understands that the reason for the refusal to perform an operation is due to throttling. The client application can wait for a period before retrying the request.
  • Throttling can be used as a temporary measure while a system autoscales. In some cases it’s better to simply throttle, rather than to scale, if a burst in activity is sudden and isn’t expected to be long lived because scaling can add considerably to running costs.
  • If throttling is being used as a temporary measure while a system autoscales, and if resource demands grow very quickly, the system might not be able to continue functioning—even when operating in a throttled mode. If this isn’t acceptable, consider maintaining larger capacity reserves and configuring more aggressive autoscaling.

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