AWS re:Invent 2014 | (PFC307) Auto Scaling: A Machine Learning Approach

Опубликовано 14 ноября 2014, 20:57

Auto Scaling groups used in conjunction with auto-scaling policies define when to scale out or scale in instances. These policies define actionable states based on a defined event and time frame (e.g., add instance when CPU utilization is greater than 90% for 5 consecutive minutes). In this session, Electronic Arts (EA) discusses a pro-active approach to scaling. You learn how to analyze past resource usage to help pre-emptively determine when to add or remove instances for a given launch configuration. Past data is retrieved via Amazon CloudWatch APIs, and the application of supervised machine learning models and time series smoothing is discussed.