Combining Step Scaling and Target Tracking in ECS Fargate for Optimal Scaling

Scaling strategies are critical to the performance and cost-efficiency of containerized applications running on Amazon ECS Fargate. While ECS provides robust scaling capabilities, many teams struggle to balance responsiveness with stability. A common pain point is that target tracking scaling policies, which use CloudWatch metrics to adjust capacity automatically, often react too slowly to sudden spikes in demand due to their reliance on 3-minute data points. This delay can lead to resource bottlenecks and degraded performance.

By combining step scaling and target tracking, you can achieve faster, more responsive scaling while still maintaining steady-state efficiency. Here, we’ll explore the benefits of this hybrid approach and provide actionable insights for implementation.

Understanding the Basics of Target Tracking and Step Scaling

Target Tracking Scaling

Target tracking scaling policies are akin to a thermostat: they adjust your container capacity to maintain a predefined target metric, such as CPU utilization or request count per target. While highly automated and effective for maintaining steady performance, they rely on 3-minute CloudWatch data points. This means:

• Target tracking works well for gradual changes in demand.
• It struggles to address sudden traffic spikes, as scaling decisions are delayed by the interval.

Step Scaling

Step scaling, on the other hand, triggers scaling actions based on single data points crossing predefined thresholds. For example, if CPU utilization exceeds 80%, you might scale up by adding a specific number of containers. While step scaling reacts faster, it can be less precise over time, leading to over- or under-scaling if not carefully tuned.

The Power of Combining Step Scaling and Target Tracking

By using step scaling and target tracking together, you can mitigate the limitations of each approach. Here’s how this combination works:

1. Step Scaling for Immediate Response
   • Scenario: A sudden traffic spike causes CPU utilization to exceed 90%.
   • Response: Step scaling kicks in, immediately adding more containers to handle the load. This prevents delays caused by the 3-minute data intervals of target tracking.
2. Target Tracking for Steady-State Optimization
   • Scenario: Once the traffic stabilizes, target tracking takes over, ensuring the number of running containers aligns with the desired utilization level.
   • Response: Target tracking gradually adjusts capacity, avoiding excessive overprovisioning and optimizing costs.

Implementation Tips

To maximize the effectiveness of this hybrid approach, follow these best practices:

1. Define Clear Thresholds for Step Scaling
   • Use precise metrics like CPU utilization, memory utilization, or request count to define scaling thresholds.
   • Set thresholds that complement your target tracking policies without conflicting.
2. Enable Soci Indexes
   • If you’re using Amazon ECS with EC2, enable Soci indexes to speed up image loading times, allowing containers to spin up faster during scaling events.
3. Pre-Build Caches
   • For ECS on EC2, use a caching layer or pre-warm EC2 instances to reduce container startup time. This ensures your system can handle demand spikes while containers initialize.
4. Monitor and Tune Regularly
   • Regularly review CloudWatch metrics to ensure your scaling policies are meeting both performance and cost goals.
   • Fine-tune step scaling actions to handle spikes without overshooting.
5. Leverage Scaling Cooldowns
   • Configure cooldown periods for both step scaling and target tracking to avoid redundant scaling actions during high-traffic periods.

Benefits of a Hybrid Scaling Strategy

• Faster Response to Spikes: Step scaling ensures your application can handle traffic surges immediately, avoiding downtime or degraded performance.
• Improved Cost Efficiency: Target tracking optimizes capacity over time, preventing overprovisioning after demand subsides.
• Enhanced User Experience: By minimizing latency during scaling events, you ensure a smoother experience for your users.
• Flexibility for Different Workloads: The combination works well for applications with variable traffic patterns, from e-commerce platforms to real-time analytics systems.

Conclusion

Scaling in ECS Fargate requires balancing responsiveness and cost efficiency. By combining step scaling and target tracking, you can handle sudden demand spikes while maintaining steady-state performance. Enable SOCI indexes or build caches on EC2 to further optimize container spin-up times if you’re running ECS on EC2. With this hybrid approach, your scaling strategy becomes a powerful tool to meet demand, improve user experience, and manage costs effectively.