Introduction to EC2 Auto Scaling
EC2 Auto Scaling is a mechanism in AWS that automatically adjusts the number of EC2 instances in a fleet based on defined metrics and thresholds.
It helps meet demand by scaling resources up or down, optimizing performance and costs.
Use Case Example:
Example scenario: Single EC2 instance acting as a web server.
Increased demand leads to higher CPU utilization.
EC2 Auto Scaling can be configured to launch a second instance when CPU utilization reaches a defined threshold (e.g., 75%).
Load balancing helps evenly distribute traffic, avoiding performance issues.
As demand decreases, Auto Scaling can be configured to terminate instances, optimizing costs.
Key Advantages of EC2 Auto Scaling:
Automation: Automatic provisioning based on custom-defined thresholds.
Greater Customer Satisfaction: Ensures capacity is provisioned to meet demand, reducing the likelihood of performance issues.
Cost Reduction: Automatically scales resources up or down, optimizing costs by only paying for resources when they are running (on a per-second basis).
Scalable and Flexible Architecture: Coupling Auto Scaling with Elastic Load Balancer enhances scalability and flexibility.
Overview of Auto Scaling Manipulation Methods
Auto Scaling is a fundamental component for elastic and fault-tolerant architectures in AWS.
Often taken for granted, it plays a crucial role in managing resources effectively.
Complexity of Auto Scaling:
Despite its common use, autoscaling involves complexities that impact performance and consistency.
Setting up policies correctly is essential for optimal performance.
Four Ways to Modify Instances in an Auto Scaling Group:
Manual Adjustment:
Modify top and bottom bounds or the desired instance count.
Pros: Explicit control, useful for predictable, non-fluctuating workloads.
Cons: Requires manual intervention, not suitable for highly dynamic workloads.
Scheduled Scaling:
Add or subtract instances based on a predefined schedule (e.g., certain times of the day).
Pros: Predictable scaling based on known patterns.
Cons: May not adapt well to sudden changes in demand.
Dynamic Scaling:
Automatic scaling that adds or removes instances based on actual demand.
Pros: Automatically adjusts to actual demand, well-suited for dynamic workloads.
Cons: May lead to scaling events triggered by short-term spikes.
Predictive Scaling:
Uses machine learning to understand average loads and provisions instances based on training data.
Pros: Utilizes machine learning for proactive provisioning.
Cons: Requires sufficient training data, may be less effective with highly variable workloads.
Importance of Understanding When to Use Each Method:
Each scaling method has its own strengths and weaknesses.
Choosing the right method depends on the nature of the workload and its demand patterns.
Manual Scaling in Auto Scaling Groups
Need for Manual Scaling:
In certain scenarios, manual intervention in Auto Scaling becomes necessary.
One example is anticipating a large spike in traffic, where proactive adjustments can prevent performance issues.
Use Case Example:
Scenario: Launching a new marketing campaign during a major event with millions of viewers.
Need: Scale up the fleet ahead of time to handle the surge in traffic and prevent user experience issues.
Strategy: Manually adjust the desired, minimum, and maximum number of instances in the Auto Scaling group.
Advantages of Manual Scaling:
Proactive Management: Allows getting ahead of potential issues before they occur.
User Experience: Reduces downtime for end users during anticipated high-demand periods.
Flexibility: Enables manual adjustments to scale back if over-provisioned.
Limitations of Manual Scaling:
Scalability: Not a long-term, scalable solution.
Suitable for occasional, planned events or scenarios.
Operational Overhead: Manual adjustments can be cumbersome for daily operations.
Ideal for infrequent, high-impact events.
Example Scenario: Marketing Campaign Launch:
Event: Running an ad during a major sporting event.
Objective: Prevent user experience issues due to traffic surge.
Actions:
Manually adjust desired, minimum, and maximum instances in the Auto Scaling group.
Scale up ahead of the event and scale back to normal levels post-event.
Considerations:
Manual scaling is a strategic solution for specific scenarios.
Not suitable for routine, day-to-day scaling requirements.
Balances proactive management with the need for hands-on intervention during critical events.
Dynamic Scaling in AWS Auto Scaling
Overview of Dynamic Scaling:
Dynamic scaling is the core of autoscaling in AWS, automating instance adjustments based on defined metrics.
Two main types: Step Scaling and Target Tracking.
Step Scaling:
Metric Tracking: Typically tracks CPU utilization of the entire autoscaling group.
Upper Bound: Adding instances when metric exceeds a specified upper threshold (e.g., 80% CPU).
Lower Bound: Removing instances when metric falls below a specified lower threshold (e.g., 20% CPU).
Cooldown Period: A waiting period to avoid rapid scaling; allows new instances to come online.
CloudWatch Alarms: Thresholds trigger scaling events; multiple alarms with varying thresholds are possible.
Preventing Overscaling: Checks for ongoing scaling events to prevent adding instances on top of each other.
Target Tracking:
Metric Selection: Similar to Step Scaling, often using CPU utilization.
Simplified Approach: Set a target value (e.g., 40% CPU), and AWS manages alarms and scaling automatically.
Aggressive Scaling Up: System scales aggressively when metrics deviate significantly from the target.
Slow Scaling Down: Ensures stability and availability by gradually reducing instances.
Small Autoscaling Groups: Challenges in maintaining tracked metrics due to significant capacity swings with each instance addition or removal.
Important Note: Do not delete CloudWatch alarms created by target tracking, as it disrupts functionality.
Cooldown and Scaling Strategy:
Cooldown Importance: Essential to avoid overscaling and unnecessary costs.
Scaling Strategy: Suggested to scale up more aggressively to address load issues and scale down gradually to ensure stability.
Historical Note: Cooldowns were more critical when instances were billed hourly, avoiding excessive billing for short-lived instances.
Conclusion:
Step Scaling vs. Target Tracking: Step scaling provides more control but requires manual setup, while target tracking is simpler and more automated.
Considerations: Both methods adapt to changing workloads and optimize resource utilization.
Autoscaling Group Size: Impact on the effectiveness of target tracking due to capacity swings in smaller groups.
Best Practices:
Proactive Scaling: Manually adjust scaling for expected events to prevent user experience issues.
Balancing Act: Balancing aggressive scaling for demand and gradual scaling to avoid disruption.
Monitoring CloudWatch Alarms: Vital for understanding autoscaling behavior and troubleshooting.
Policy Deletion Caution: Avoid deleting CloudWatch alarms for target tracking to maintain functionality.
Predictive Scaling in AWS Auto Scaling
Overview of Predictive Scaling
Predictive scaling aims to get ahead of system load by provisioning instances before an event occurs.
Utilizes machine learning to understand traffic patterns and forecast future needs.
Particularly effective for cyclical and recurring workloads.
Key Features:
Machine Learning: Learns traffic patterns, predicting when load will increase or decrease.
Cyclical Traffic: Well-suited for predictable, repetitive traffic patterns (e.g., business hours, nights, weekends).
Recurring Workloads: Useful for batch processing, analytics, and other regularly scheduled tasks.
Data Source: Uses CloudWatch metrics, requiring at least 24 hours of historical data, with a look-back capability of up to 14 days.
Forecast Updates: Daily updates to the forecast based on the latest CloudWatch metric data.
Forecast-Only Mode:
Risk-Free Testing: Allows running predictive auto scaling in forecast-only mode without taking any actions.
Visualization: Users can compare forecasted predictions with actual data through the EC2 Auto Scaling console.
Graphical Representation: Provides a graph to visualize the forecast and actual performance.
Switching to Forecast and Scale Mode:
Approval Process: If satisfied with forecast results, users can switch to forecast and scale mode for full predictive auto scaling functionality.
Provisioning Instances: Scales the number of instances at the beginning of every hour.
Real-Time Consideration: May not be as real-time as some users might expect.
Coexistence with Dynamic Scaling:
Combined Approach: Predictive auto scaling and dynamic auto scaling can be used together for a more refined approximation.
Tuning Required: Fine-tuning necessary for optimal performance.
Cost Implications: While providing higher availability, using both methods may incur additional costs.
Considerations and Recommendations:
Patterns and Workloads: Best suited for traffic patterns with clear cycles and recurring workloads.
Forecast Accuracy: Depends on historical data and regular updates.
Cost-Benefit Analysis: Consider the trade-off between higher availability and potential additional costs.
Tuning and Optimization: Requires tuning to strike the right balance in meeting user needs efficiently.
Conclusion:
Comprehensive Solution: Predictive scaling, when used in conjunction with dynamic scaling, offers a comprehensive approach to meet varying workload demands.
User Satisfaction: Balances resource provisioning with user demand to enhance overall user experience.
Flexibility: Users have the flexibility to choose the level of automation based on their specific needs and comfort with forecast results.
Scheduled Scaling in AWS Auto Scaling
Definition and Purpose:
Scheduled Scaling: Adding or removing instances from auto scaling groups based on predefined time parameters.
Purpose: Efficiently manage costs by scheduling instances to be active only during specific time periods.
Use Cases:
Batch Processing: Schedule instances for batch processing during times of lower spot instance prices.
Dev Environments: Turn off development and test environments after business hours to reduce costs.
Combination with Dynamic Scaling: Use scheduled scaling in combination with dynamic scaling for optimal resource utilization.
Cost Reduction Strategies:
Spot Instances: Effective when combined with spot instances to capitalize on cost savings.
Turning Off Unused Resources: Turn off instances during idle periods, reducing unnecessary costs.
Flexible Application:
Dev and Test Environments: Turn off non-production environments during off-hours to save resources and costs.
Dynamic Scaling Integration: Combine with dynamic scaling for adaptability during peak times and efficiency during low-demand periods.
Cost Savings Considerations:
Optimizing Spot Instances: Well-suited for architectures that leverage the cost benefits of spot instances.
Handling Start and Stop Operations: Requires solutions capable of handling the stopping and starting of instances.
Complementary Scaling Mechanisms:
Combination with Dynamic Scaling: Achieve a balance by using scheduled scaling for planned, predictable changes and dynamic scaling for real-time adjustments.
Example: Dev and test environments scheduled to turn on and off, while dynamic scaling manages fluctuations during working hours.
Implementation and Architecture:
Setting Time Parameters: Configure schedules based on specific hours or time intervals.
Integration: Can be used in conjunction with other scaling mechanisms for comprehensive resource management.
Benefits:
Cost Savings: Efficiently allocate resources only when needed, reducing overall infrastructure costs.
Resource Optimization: Ensure optimal resource utilization by aligning capacity with anticipated demand.
Flexibility: Provides flexibility in managing different types of workloads and environments.
Considerations:
Spot Instance Compatibility: Works effectively with spot instances but requires solutions capable of handling interruptions.
Monitoring and Adjustments: Regular monitoring and adjustments to schedules may be needed based on evolving workload patterns.
Conclusion:
Strategic Resource Management: Scheduled scaling enhances cost efficiency by strategically activating and deactivating instances based on anticipated workload patterns.
Holistic Scaling Approach: When combined with other scaling mechanisms, offers a holistic approach to auto scaling that caters to various operational and cost considerations.
Adaptability: Provides adaptability to diverse architectural requirements, contributing to overall resource and cost optimization.