Adaptive Handling of Dynamic Conditions
In the context of a cloud-native application with micro-services deployed on sharedinfrastructure, adaptive management is crucial based on the scale andresponsiveness requirements of the application:Effective capacity planning can handle scale needs. Frequent measurement, even under extreme load conditions, can manage response requirements effectively.
To illustrate, let’s consider a scenario where the application needs to handle 500 million transactions during peak hours (9am to 11am) and 50 million at other times, indicating varying scale needs throughout the day. The responsiveness target is 50ms, even during peak load.
Given the stringent SLO targets, quick actions are essential for scaling efficiently. Pre-provisioning services using Runbooks or Playbooks, but the cost-effectiveness of this approach depends on the time required for provisioning and anticipated scale requirements. Monitoring scale will be laborious and costly and optimizing resources during non-peak hours is crucial for cost control. The challenge with trigggering resource scaling using the Runbooks or Playbooks is to correctly estimate the scale requirements based on demand. Capturing the demand requirements requires real-time monitoring and adjusting resources appropriately.
Utilizing AIMSLO enables automated scaling actions based on environmental conditions and usage patterns, ensuring SLO targets are met by dynamically adjusting resources. The AI-driven decision-making process optimizes costs by scaling resources just-in-time. In cases of critical incidents or service disruptions, the human-in-the-loop feature facilitates rapid escalation and learning for the AI model, leading to timely resolutions with minimal business impact.