Our presentation (with Jignesh Shah) was accepted for www.CMGimpact.com
Scale in Clouds
What, How, Where, Why and When to Scale
Igor Trubin, Jignesh Shah - Capital One bank
ABSTRACT
Presentation includes the following discussion themes.
What to scale: servers, databases, containers, load balancers.
How to scale: horizontally/rightsizing, vertically, manually, automatically, ML based, predictive, serverless.
Where to scale: AWS (ASG,ECS, EKS, ELB), AZURE, GCP, K8s.
Why to scale: cost optimization, incidents avoidance, seasonality.
When to scale: auto-scaling policies and parameters, pre-warming to fight latency, correlating with business/app drivers.
Presentation includes a user case study of scaling parameters optimization: monitoring, modeling and balancing vertical and horizontal scaling, calculating optimal initial/desired cluster size and more.
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The article discusses cloud scaling strategies that help organizations optimize application performance, availability, and infrastructure costs across modern cloud environments. It explores what components should be scaled, different scaling approaches such as horizontal, vertical, predictive, and serverless scaling, and where these techniques can be implemented using platforms like AWS, Azure, GCP, and Kubernetes. Effective scaling strategies enable businesses to handle fluctuating workloads while maintaining high availability and operational efficiency.
Cloud computing provides the foundation for scalable, resilient, and highly available enterprise applications by offering automated resource management, container orchestration, load balancing, and elastic infrastructure. Understanding cloud architecture and scaling mechanisms enables developers to design systems that efficiently adapt to changing business demands. Students interested in implementing cloud-native solutions can explore Cloud Computing Projects, featuring practical implementations of cloud infrastructure, virtualization, Kubernetes, microservices, and distributed applications.
Intelligent scaling is increasingly supported by machine learning techniques that analyze historical workloads, predict resource demand, and automate infrastructure provisioning. Combining predictive analytics with cloud orchestration helps organizations improve performance while minimizing operational costs and resource wastage. Those looking to build AI-powered cloud solutions can further explore Machine Learning Projects for Final Year, showcasing practical implementations of predictive analytics, intelligent automation, and cloud-based AI applications.
Readers interested in exploring modern cloud architectures and scalable computing solutions can also refer to Cloud Computing Projects, which presents practical project ideas covering cloud deployment, virtualization, container orchestration, distributed systems, and enterprise-scale cloud applications.
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