Predictive Scaling

Predictive Scaling

Scale safely by automating more functions to optimize performance


Problem:
A retailer with a large e-commerce site struggles with huge peaks in demand followed by big drops. These can be caused by a number of business events, such as a large sale, new product introduction, or times like weekends and evenings. Predicting these peaks and valleys are difficult, since they are usually business driven, not driven by technology.

They previously overprovisioned resources, which meant high cloud costs, especially during periods of low demand. Kubernetes’ Horizontal Pod Autoscaler (HPA) isn’t working how they hoped to handle the changes in traffic – it scales based on thresholds, and given the nature of their traffic spikes, it can’t scale up to the correct number of replicas in time, causing performance degradation.

HPA scales based on pre-determined thresholds, and given the nature of their traffic spikes, it fails to scale up the correct number of replicas fast enough, causing performance degradation.

Goal:
They need a solution that can learn the patterns of their e-commerce site and have the capacity ready for any traffic spikes in real-time.

We are shaping the Aspen Mesh Solution now:

Our focus is to ensure your application is always performant and scaled to the correct number. Instead of waiting to scale when a threshold is met or over-provisioning, our predictive scaling will allow you to scale ahead of demand, optimizing your resource utilization and controlling cloud costs. We combine observability data from your service mesh and Kubernetes workloads with machine learning to provide reliable autoscaling and recommend (and later automate) real-time configuration changes to ensure application performance and resiliency. 

We believe optimal performance relies on leveraging all the data that affects your app’s behavior. For scaling, this means no longer guessing when demand may go up or not reacting to threshold alerts in time.

  • Proactively autoscale to the correct workloads. 
  • ID and remediate potential issues before they impact app availability or performance. 
  • Continually adjust container workloads based on usage or behavior changes in your applications. 
  • No longer rely on static, pre-configured thresholds or dashboards and human interpretation. 

Are you ready to tackle the problem of scaling safely by automating more functions to optimize performance? Then we’d like to hear from you. 

Request Early Access now  – Get unlimited access for one year and all new features as they are released.

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Become a Lighthouse User

We’re looking for modern app teams operating at scale who recognize they have to adapt how they work to excel. We want you to share your biggest problems and help us shape features of our SaaS platform.

Aspen Mesh is looking for teams ready to leverage the power of the technologies in their microservices environment to  optimize application performance.

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