Add or remove compute capacity to meet changes in demand.
Whether you are running one Nobus FCS instance or thousands, you can use Nobus FCS Auto Scaling to detect impaired Nobus FCS instances and unhealthy applications, and replace the instances without your intervention. This ensures that your application is getting the compute capacity that you expect. Nobus FCS Auto Scaling will perform three main functions to automate fleet management for FCS instances:
Scaling based on a schedule allows you to scale your application ahead of known load changes. For example, every week the traffic to your web application starts to increase on Wednesday, remains high on Thursday, and starts to decrease on Friday. You can plan your scaling activities based on the known traffic patterns of your web application.
Nobus FCS Auto Scaling enables you to follow the demand curve for your applications closely, reducing the need to manually provision Nobus FCS capacity in advance. For example, you can use target tracking scaling policies to select a load metric for your application, such as CPU utilization. Or, you could set a target value using the new “Request Count Per Target” metric from Application Load Balancer, a load balancing option for the Elastic Load Balancing service. Nobus FCS Auto Scaling will then automatically adjust the number of FCS instances as needed to maintain your target.
Predictive Scaling, a feature of Nobus Auto Scaling uses machine learning to schedule the right number of FCS instances in anticipation of approaching traffic changes. Predictive Scaling predicts future traffic, including regularly-occurring spikes, and provisions the right number of FCS instances in advance. Predictive Scaling’s machine learning algorithms detect changes in daily and weekly patterns, automatically adjusting their forecasts. This removes the need for manual adjustment of Auto Scaling parameters as cyclicality changes over time, making Auto Scaling simpler to configure. Auto Scaling enhanced with Predictive Scaling delivers faster, simpler, and more accurate capacity provisioning resulting in lower cost and more responsive applications.