Hey everyone! I'm looking to better understand how to rightsize cloud resources across various services—not just computing instances like VMs and containers, but also databases, caches, storage, networking components, API gateways, and other PaaS offerings. I'm facing a few challenges:
1. How do I decide based on real usage metrics (like CPU, memory, network throughput, requests, and connections) when it's appropriate to recommend downsizing or optimization?
2. What specific thresholds or best practices should I consider for different resource types?
For example, if a PostgreSQL database consistently has CPU usage under a certain percentage and connection counts remain low, would downsizing be advisable? Similarly, for a Redis cache with low memory and CPU usage over time, should I recommend a smaller service plan?
I've come across some tools like Azure Advisor and AWS Compute Optimizer, but they mostly focus on compute resources and not on PaaS components. I would love to hear any experiences, methodologies, or rules of thumb that you or your organization follow for this kind of rightsizing. Any whitepapers, blog posts, or internal heuristics would be greatly appreciated! Thanks a ton!
3 Answers
I don't have a specific framework, but I can offer some thoughts. When rightsizing, it's important to leave some headroom for scaling up. Generally, the cost associated with resources is inversely related to utilization. For instance, cutting down too aggressively could lead to higher costs later if you need to scale back up quickly. Understanding your traffic patterns and how quickly you can increase capacity is essential.
Real-world testing is key! Performance tests can help outline usage patterns and inform capacity planning decisions. Analyze the overall patterns of usage to see when scaling up or down is appropriate. For specific services, consider developing internal rules of thumb based on historical data, like keeping CPU usage under 40% for a managed PostgreSQL database before recommending a downsizing.
You might want to check out the KEDA documentation for insights. When it comes to scaling, it really depends on your metrics and understanding what they mean. I’ve seen cases where a coworker thought they had capacity because their cluster was using 50% of the CPUs, but they were actually maxed out on node performance. Creating meaningful dashboards can help clarify these issues and guide your decisions about rightsizing.
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