Scaling Apps

Cerebrium’s scaling system automatically manages computing resources to match app demand. The system handles everything from a few simple requests, to the processing of multiple requests simultaneously, while optimizing for both performance and cost.

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How Autoscaling Works

The scaling system monitors two key metrics to make scaling decisions:

The number of requests currently waiting for processing in the queue indicates immediate demand. Additionally, the system tracks how long each request has waited in the queue. When either of these metrics exceeds their thresholds, new instances start within 3 seconds to handle the increased load.

As traffic decreases, instances enter a cooldown period after processing their last request. When no new requests arrive during cooldown, instances terminate to optimize resource usage. This automatic cycle ensures apps remain responsive while managing costs effectively.

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Scaling Configuration

The cerebrium.toml file controls scaling behavior through several key parameters:

[cerebrium.scaling]
min_replicas = 0           # Minimum running instances
max_replicas = 3           # Maximum concurrent instances
cooldown = 60              # Cooldown period in seconds
replica_concurrency = 1    # The maximum number of requests each replica of an app can accept

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Minimum Instances

The min_replicas parameter defines how many instances remain active at all times. Setting this to 1 or higher maintains warm instances ready for immediate response, eliminating cold starts but increasing costs. This configuration suits apps that require consistent response times or need to meet specific SLA requirements.

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Maximum Instances

The max_replicas parameter sets an upper limit on concurrent instances, controlling costs and protecting backend systems. When traffic increases, new instances start automatically up to this configured maximum.

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Cooldown Period

After processing a request, instances remain available for the duration specified by cooldown. Each new request resets this timer. A longer cooldown period helps handle bursty traffic patterns but increases instance running time and cost.

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Replica Concurrency

The number of requests an app instance can handle concurrently is dictated by the replica_concurrency parameter. This is a hard limit, and an individual replica will not accept more than this limit at a time. By default, once this concurrency limit is reached on an instance and there are still requests to be processed in-flight, the system will scale out by the number of new instances required to fulfil the in-flight requests. For example, if replica_concurrency=1 and there are 3 requests in flight with no replicas currently available, Cerebrium will scale out 3 instances of the application to meet that demand.

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Processing Multiple Requests

Apps can process multiple requests simultaneously using Cerebrium’s batching and concurrency features. The system offers native support for frameworks with built-in batching capabilities and enables custom implementations through the custom runtime feature. For detailed information about handling multiple requests efficiently, see our Batching & Concurrency Guide.

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Instance Management

Cerebrium ensures reliability through automatic instance health management. The system restarts instances that encounter issues, quickly starts new instances to maintain processing capacity, and monitors instance health continuously.

Apps requiring maximum reliability often combine several scaling features:

[cerebrium.scaling]
min_replicas = 2              # Maintain redundant instances
cooldown = 600                # Extended warm period
max_replicas = 10             # Room for traffic spikes
response_grace_period = 1200  # Maximum request lifespan ensuring graceful exit

The response_grace_period parameter stipulates how long in seconds a request would need at most to finish, and provides time for instances to complete active requests during normal operation and shutdown. During normal replica operation, this simply corresponds to a request timeout value. During replica shutdown, the Cerebrium system sends a SIGTERM signal to the replica, waits for the specified grace period, issues a SIGKILL command if the instance has not stopped, and kills any active requests with a GatewayTimeout error.

Performance metrics available through the dashboard help monitor scaling behavior:

• Request processing times
• Active instance count
• Cold start frequency
• Resource usage patterns

The system status and platform-wide metrics remain accessible through our status page, where Cerebrium maintains 99.9% uptime.

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Using Scaling Metrics

Cerebrium provides a variety of scaling criteria which may be used to scale apps according to different metrics. As mentioned above, by default this is determined by an application’s replica_concurrency. However, this strategy may be insufficient for some use cases and so Cerebrium currently provides four scaling metrics to choose from:

• concurrency_utilization
• requests_per_second
• cpu_utilization
• memory_utilization

These can be added to the cerebrium.scaling section as such, by specifying one of these metrics and a target:

[cerebrium.scaling]
min_replicas = 0
cooldown = 600
max_replicas = 10
response_grace_period = 120
replica_concurrency = 1
scaling_metric = "concurrency_utilization"
scaling_target = 100

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Concurrency Utilization

concurrency_utilization is the default scaling metric, and defaults to a target of 100% if not set explicitly. This scaling metric works by maintaining a maximum percentage of your replica_concurrency averaged across every instance of the app. For example, if an application has replica_concurrency=1 and scaling_target=70, Cerebrium will attempt to maintain 0.7 requests per instance across your entire deployed service. This strategy would always ensure an extra 30% capacity is provisioned.

As a different example, say an app has replica_concurrency=200 and scaling_target=80. In this case, Cerebrium will maintain 160 requests per instance, and will begin to scale out once that target has exceeded.

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Requests per Second

requests_per_second is straightforward criterion which aims to maintain a maximum application throughput measured in requests per second averaged over every application instance. This can be a more effective scale metric than concurrency_utilization if appropriate performance evaluation has been done on the application to determine the throughput. This criterion is not recommended for most GPU applications, since this scaling metric does not enforce concurrency limits. For example, if scaling_target=5, Cerebrium will attempt to maintain a 5 requests/s average across all app instances.

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CPU Utilization

cpu_utilization uses a maximum CPU percentage utilization averaged over all instances of an application to scale out, relative to the cerebrium.hardware.cpu value. For example, if an application has cpu=2 and scaling_target=80, Cerebrium will attempt to maintain 80% CPU utilization (1.6 CPUs) per instance across your entire deployed service. Since there is no notion of scaling relative to 0 CPU units, it is required that min_replicas=1 if using this metric.

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Memory Utilization

memory_utilization uses a maximum memory percentage utilization averaged over all instances of an application to scale out, relative to the cerebrium.hardware.memory value. Note this refers to RAM, not GPU VRAM utilization. For example, if an application has memory=10 and scaling_target=80, Cerebrium will attempt to maintain 80% Memory utilization (8GB) per instance across your entire deployed service. Since there is no notion of scaling relative to 0GB of memory, it is required that min_replicas=1 if using this metric.

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Keeping a Scaling Buffer

In certain use-cases where app startup time or total request time is long and traffic is predictable, a consistent excess capacity which is dynamically added to the app’s current required capacity may be needed to meet adequate request throughput. Cerebrium provides a replica buffer mechanism to cater for this, whereby the scaling system aims to keep a specific excess replica capacity available at all times above what the scaling metric suggests. This is done through the scaling_buffer option in the config. Currently, this is only available when using the following scaling metrics:

• concurrency_utilization
• requests_per_second

The buffer can be added to the cerebrium.scaling section as such, by specifying scaling_buffer:

[cerebrium.scaling]
min_replicas = 0
cooldown = 600
max_replicas = 10
response_grace_period = 120
replica_concurrency = 1
scaling_metric = "concurrency_utilization"
scaling_target = 100
scaling_buffer = 3

To illustrate how this works using the above config, if this application receives no traffic, it will buffer out to 3 replicas of capacity as a baseline. This is the case even though min_replicas=0, since scaling_buffer is actually modifying the suggested replica count from the autoscaler of 0, and 0+3=3. Since the config has specified 100 as a target for concurrency_utilization and replica_concurrency=1, if the application now receives 1 request the autoscaler will suggest a value of 1 replica for scale out. Since however, we have scale_buffer=3, the application will actually scale one more replica to (1+3)=4. In other words, the scale buffer will simply add a static amount of replicas to the number of replicas the autoscaler suggests using the scale target. Once this request has completed, the usual cooldown period will apply, and the app replica count will scale down back to the baseline of 3 replicas.