Auto-scaling is the automatic adjustment of compute resources based on demand. Rather than manually provisioning fixed infrastructure, auto-scaling systems monitor demand metrics and automatically launch or terminate instances to maintain performance whilst optimising costs. Auto-scaling enables applications to handle traffic spikes without manual intervention whilst reducing costs during low-traffic periods.
Auto-Scaling Benefits
Auto-scaling provides substantial advantages:
Cost reduction - Resources are allocated dynamically. Excess capacity is removed during low-demand periods.
Performance consistency - Resources scale with demand, maintaining consistent response times.
Operational efficiency - No manual provisioning required. Scaling happens automatically.
High availability - Distributing across multiple availability zones and regions provides redundancy.
Flexibility - Easily scaling up or down for changing requirements.
Scaling Policies
Different policies govern scaling decisions:
Target tracking - Maintaining a target metric (CPU at 70 per cent, response time at 100ms). Most common approach.
Step scaling - Scaling based on metric thresholds (if CPU above 80 per cent, add 5 instances).
Scheduled scaling - Scaling based on time schedules (scale up at 9am, down at 6pm).
predictive scaling - Using machine learning to predict demand and scale preemptively.
Custom scaling - Writing custom logic for specific scaling rules.
Target tracking is simplest and most effective for most applications.
Scaling Metrics
Different metrics trigger scaling:
CPU utilisation - Scaling based on processor usage. Common but can be misleading if some CPUs are idle.
Memory utilisation - Scaling based on memory usage.
Network throughput - Scaling based on data transfer.
Application metrics - Custom application metrics (queue depth, request latency).
Custom CloudWatch metrics - Application-reported metrics for sophisticated scaling decisions.
Choosing appropriate metrics is critical for effective scaling.
Scaling Triggers
Metrics trigger scaling through alarms:
Scale-up triggers - When metrics exceed thresholds, launch additional instances.
Scale-down triggers - When metrics fall below thresholds, terminate instances.
Cooldown periods - Preventing flapping by waiting before scaling again after a scaling event.
Grace periods - New instances are not considered for scaling decisions immediately after launch.
Proper trigger configuration prevents excessive scaling whilst responding to demand changes.
Auto-Scaling at PixelForce
PixelForce designs applications with auto-scaling in mind. AWS Auto Scaling Groups manage instance count based on demand. Applications are designed stateless, enabling instances to be added or removed transparently. This approach enables cost-effective capacity that responds to demand.
Scaling Limitations
Auto-scaling has limitations:
Launch time - New instances take time to launch and become ready. During rapid demand spikes, scaling cannot keep up.
Predictability - Scaling decisions are reactive. Sudden demand spikes may cause brief performance degradation before scaling responds.
Costs - Scaling up costs money. Optimising scaling policies balances performance and cost.
Database bottlenecks - If databases become bottlenecks, application scaling may not help.
Scaling Databases
Databases are often scaling bottlenecks:
Read replicas - Distributing read traffic across replica databases.
Write scaling - Database sharding distributes writes but is complex.
Caching - Reducing database load through caching.
Managed databases - Cloud-managed databases handle some scaling automatically.
Vertical vs. Horizontal Scaling
Different scaling approaches:
Vertical scaling - Adding more resources (CPU, memory) to existing instances. Limited by hardware maximums.
Horizontal scaling - Adding more instances. Requires stateless design but enables unlimited scaling.
Most modern applications use horizontal scaling for unlimited capacity.
Load Balancing with Auto-Scaling
Auto-scaling requires load balancing:
Dynamic registration - New instances automatically join load balancers.
Health checks - Failed instances are automatically removed from load balancers.
Transparent scaling - Clients do not need to know about scaling. Load balancers handle routing.
Monitoring Auto-Scaling
Proper monitoring ensures effective scaling:
Scaling events - Monitoring scaling activity to verify scaling is working.
Metric tracking - Monitoring metrics that trigger scaling.
Performance impact - Verifying scaling maintains performance.
Cost monitoring - Verifying scaling does not increase costs excessively.
Scaling Policies Optimisation
Effective scaling requires tuning:
Metric selection - Choosing metrics closely correlated with load.
Threshold tuning - Setting thresholds that trigger scaling appropriately.
Cooldown periods - Balancing responsiveness and stability.
Gradual scaling - Scaling gradually rather than all at once to enable monitoring.
Predictive Scaling
Advanced approaches predict demand:
Machine learning models - Training models on historical data to predict future demand.
Preemptive scaling - Scaling in advance of predicted demand spikes.
Improved performance - Avoiding performance degradation from reactive scaling.
Complexity - Requires historical data and model training.
Cost Optimisation
Auto-scaling enables cost optimisation:
Right-sizing - Instances are sized for typical load, not peak load.
Spot instances - Using cheaper spot instances for scaling when appropriate.
Reserved instances - Base capacity through reserved instances, scaling with spot instances.
Scheduled scaling - Reducing capacity during predictable low-demand periods.
Conclusion
Auto-scaling automatically adjusts infrastructure capacity based on demand, improving performance whilst optimising costs. By defining appropriate metrics and policies, monitoring scaling, and optimising configurations, organisations build systems that respond flexibly to changing demand without manual intervention.