You can also set the minimum and maximum instance counts (to help manage costs)Īzure Using captured images (of a marketplace product) Step 1 And Queue Scale Down: If < 1 message over X minutes, decrease by 1 instance. And SQS Scale Down: If 1 message over X minutes, increase by 1 instance. If you choose a SQS scaling policy, choose a Policy type of Simple Scaling, which is compatible with CloudWatch ALARMS.Ĭreate 2 CloudWatch Alarms, and connect them to your AutoScaling Group (use SQS:ApproximateNumberOfMessagesVisible for your logic).Ĭreate 2 Scaling Policies: SQS Scale Up: If > 1 message over X minutes. Set the Scaling Policies to determine when to scale up and down (either # of SQS messages or average CPU % will work) keeping as few workers running as possible), but quicklyĬonfigure a new worker instance (use the same admin credentials when prompted during config-openshot-cloud)Ĭreate an Image (AMI) of the new worker instance (use a descriptive name, such as “OpenShot Cloud Worker Image”)Ĭreate a Launch Configuration (choose the AMI from step 2, choose the type of instance, and name descriptively)Ĭreate an Auto Scaling Group (choose the Launch Configuration from step 3 and name descriptively) This is an effective strategy for managing costs (i.e. The workload has ended and the queue is empty, the pool of worker instances can shrink back down to 1 (or even 0). Configure auto scaling on the worker instance,Īnd base it off of average CPU > 50% for X minutes, or the number of messages in the Queue (SQS or Azure Queue Storage).Īs your video export jobs queue up, more and more worker instances will come online and help render jobs. Both AWS and Azure offer auto scaling solutions (each with slightly different features, but generally they workĪbout the same).
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