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The autoscaling component comprises of
the synapse mediators AutoscaleInMediator and AutoscaleOutMediator and
a Synapse Task ServiceRequestsInFlightEC2Autoscaler that functions as
the load analyzer task. A system can scale up based on several factors,
and hence autoscaling algorithms can easily be written considering the
nature of the system. For example, Amazon's Auto Scaler API provides
options to scale the system with the system properties such as Load
(the timed average of the system load), CPUUtilization (utilization of
the cpu at the given instance), or Latency (delay or latency in serving
the service requests).
- AutoscaleIn mediator - Creates a unique token and puts that into a list for each message that is received.
- AutoscaleOut mediator - Removes the relevant stored token from the list, for each of the response message that is sent.
- Load Analyzer Task -
ServiceRequestsInFlightEC2Autoscaler is the load analyzer task used for
the service level autoscaling as the default. It periodically checks
the length of the list of messages based on the configuration
parameters. Here the messages that are in flight for each of the back
end service is tracked by the AutoscaleIn and AutoscaleOut mediators,
as we are using the messages in flight algorithm for autoscaling.
implements the execute() of the Synapse Task interface. Here it calls
sanityCheck() that does the sanity check and autoscale() that handles
checks the sanity of the load balancers and the services that are
load balanced, whether the running application nodes and the load
balancer instances meet the minimum number specified in the
configurations, and the load balancers are assigned elastic IPs.
nonPrimaryLBSanityCheck() runs once on the primary load
balancers and runs time to time on the secondary/non-primary load
balancers as the task is executed periodically.
nonPrimaryLBSanityCheck() assigns the elastic IP to the instance, if
that is not assigned already. Secondary load balancers checks that a
primary load balancer is running periodically. This avoids the load
balancer being a single point of failure in a load balanced services
computeRunningAndPendingInstances() computes the number of
instances that are running and pending.
ServiceRequestsInFlightEC2Autoscaler task computes the running and
pending instances for the entire system using a single EC2 API call.
This reduces the number of EC2 API calls, as AWS throttles the number of
requests you can make in a given time. This method will be used to
find whether the running instances meet the minimum number of instances
specified for the application nodes and the load balancer instances
through the configuration as given in loadbalancer.xml. Instances are
launched, if the specified minimum number of instances is not found.
handles the autoscaling of the entire system by analyzing the load of
each of the domain. This contains the algorithm - RequestsInFlight
based autoscaling. If the current average of requests is higher than
that can be handled by the current nodes, the system will scale up. If
the current average is less than that can be handled by the (current
nodes - 1), the system will scale down.
spawns new instances, and once the relevant services successfully start
running in the spawned instances, they will join the respective
service cluster. Load Balancer starts forwarding the service calls or
the requests to the newly spawned instances, once they joined the
service clusters. Similarly, when the load goes down, the autoscaling
component terminates the under-utilized service instances, after
serving the requests that are already routed to those instances.
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