Auto-scaling in Cloud Environments Questions and Answers for Viva

Answer-1: Auto-scaling automatically adjusts the number of active servers or resources based on current demand.

Answer-2: It helps optimize resource usage, improve application availability, and control costs by scaling resources up or down.

Answer-3: Horizontal scaling (adding/removing instances) and vertical scaling (increasing/decreasing resources of an instance).

Answer-4: Horizontal auto-scaling involves adding or removing instances to handle load.

Answer-5: Vertical auto-scaling means upgrading or downgrading the capacity of an existing instance.

Answer-6: Horizontal auto-scaling is most common because it's more flexible and fault-tolerant.

Answer-7: Triggers can be based on metrics such as CPU utilization, memory usage, network traffic, or custom application metrics.

Answer-8: A scaling policy defines the rules that determine when and how to scale resources.

Answer-9: Scheduled auto-scaling happens at predetermined times; dynamic auto-scaling responds to real-time metrics.

Answer-10: AWS Auto Scaling, Azure Auto Scale, and Google Cloud Autoscaler.

Answer-11: It monitors cloud resources and adjusts the number of EC2 instances based on user-defined policies.

Answer-12: A collection of instances managed as a single unit for scaling and load balancing.

Answer-13: It scales down unused resources during low demand, reducing unnecessary expenses.

Answer-14: The time interval between two consecutive scaling activities to prevent rapid scaling.

Answer-15: Yes, some cloud providers offer auto-scaling for database resources or managed database services.

Answer-16: CPU usage, memory usage, disk I/O, network throughput, and custom application metrics.

Answer-17: Reactive auto-scaling responds after load changes; predictive uses historical data to anticipate load and scale proactively.

Answer-18: It increases availability by adding resources during high demand and reducing them during low demand.

Answer-19: Load balancers distribute traffic among scaled instances to ensure efficient resource utilization.

Answer-20: Challenges include scaling delays, inaccurate metric thresholds, and potential cost spikes.

Answer-21: A set of pre-initialized instances ready to be added quickly to handle sudden traffic spikes.

Answer-22: Kubernetes uses Horizontal Pod Autoscaler to scale pods based on resource usage.

Answer-23: These define the lower and upper limits of instances allowed in a scaling group.

Answer-24: By setting appropriate cool-down periods and threshold values.

Answer-25: It automatically adjusts resources to maintain a specific metric target, such as CPU utilization.

Answer-26: Yes, auto-scaling can reduce the number of active instances when demand decreases.

Answer-27: Step scaling adjusts resources in predefined steps based on metric thresholds.

Answer-28: By simulating load conditions to observe scaling actions and performance.

Answer-29: Auto-scaling is a mechanism to achieve elasticity, which is the ability to adapt resources dynamically.

Answer-30: Yes, monitoring tools provide the metrics required to trigger auto-scaling actions.

Answer-31: Auto-scaling happens automatically based on policies, while manual scaling requires human intervention.

Answer-32: By using session persistence, external databases, or state management solutions to handle state.

Answer-33: A lifecycle hook allows you to perform custom actions during instance launch or termination.

Answer-34: It terminates instances that are no longer needed based on scaling policies, often after draining connections.

Answer-35: Yes, for high availability and fault tolerance.

Answer-36: Predictive scaling uses machine learning and historical data to forecast demand and scale resources in advance.

Answer-37: Auto-scaling requires security policies and configurations to be applied consistently across all instances.

Answer-38: Reactive responds to current load; proactive anticipates load and scales accordingly.

Answer-39: Auto-scaling can support deployment pipelines by adjusting resources during new releases.

Answer-40: It helps maintain consistent performance by ensuring sufficient resources during peak demand.

Answer-41: It may cause frequent scaling, poor performance, or resource wastage.

Answer-42: Instance refresh updates instances in a group to the latest configuration or software version.

Answer-43: Yes, serverless platforms like AWS Lambda scale automatically based on invocation rates.

Answer-44: By rapidly launching additional instances or pods to handle increased load.

Answer-45: Reserving a certain amount of resources in advance to ensure availability.

Answer-46: Charges are based on the actual usage of resources that are running, not on idle capacity.

Answer-47: It allows using different instance types within the same auto-scaling group to optimize costs and performance.

Answer-48: It is easier for stateless apps, but possible for stateful apps with proper design.

Answer-49: Using cloud provider dashboards, logs, and monitoring services like CloudWatch or Azure Monitor.

Answer-50: Define clear policies, monitor metrics closely, test scaling events, set appropriate thresholds, and plan for failover.