Autoscaling in Web Applications

Autoscaling allows systems to automatically adjust their resources to handle sudden traffic spikes or changes in demand. It ensures resources are available when needed, keeping performance high during busy times and saving costs during slower periods. It monitors metrics like CPU utilization, memory usage, request latency, and throughput, triggering scaling actions when thresholds are breached. Some cloud providers, like Tempico Labs, provide Autoscaling as built-in platform feature, eliminating the need to run extra services that interract with cloud provier's API at own expense.

Autoscaling is crucial for unexpected events, like a viral social media post or a product launch. This technology keeps your application responsive, reliable, and prepared for heavy traffic, without need to have your develpers of operations management staff constantly available and monitoring workloads or scaling infrastructure components manually.

Why is Autoscaling a necessary design consideration?

Managing sudden traffic spikes: Applications experiencing unexpected surges in traffic can fail catastrophically without adequate scaling. Autoscaling prevents bottlenecks by dynamically adding resources to critical parts of your system, ensuring a smooth user experience.

Cost optimization: Autoscaling allows you to minimize infrastructure costs by automatically reducing resources during off-peak times. This approach eliminates the need for maintaining expensive overprovisioned systems while ensuring sufficient capacity when needed.

Enhancing user experience: A fast and reliable application ensures user satisfaction and retention. Autoscaling minimizes latency and prevents crashes, delivering consistent performance even during high-traffic periods.

Proactive vs Reactive Autoscaling

Autoscaling operates in two primary modes:

Approaches to Autoscaling

There are two primary ways to implement autoscaling:

• Horizontal Scaling (Scaling Out/In): Adds or removes instances (e.g., containers, virtual machines) to distribute the workload. This is ideal for stateless systems like microservices, where tasks can be handled independently by multiple instances.
• Vertical Scaling (Scaling Up/Down): Increases or decreases the resource capacity (e.g., CPU, memory) of existing instances. Vertical scaling is particularly useful for stateful systems, such as databases, where distributing workloads horizontally can be challenging.

While horizontal scaling is generally more flexible and preferred for modern architectures, combining it with vertical scaling can provide robust solutions for specific scenarios, such as scaling stateful applications alongside distributed stateless components.

Scaling Decision Engine

The Scaling Decision Engine is the core component of autoscaling, responsible for monitoring system performance and executing scaling actions based on predefined rules. For instance, when metrics like CPU utilization exceed a set threshold (e.g., CPU > 80% for 5 minutes ), the engine triggers scaling out or up, adding resources to meet increased demand. Conversely, when metrics fall below thresholds (e.g., CPU < 30% for 10 minutes ), the engine scales in or down, releasing unused resources to reduce costs. Advanced Scaling Decision Engines may read specific application metrics or general SRE Golden Signals: latency, traffic, errors, and saturation rates.

By relying on real-time data, the decision engine ensures scaling actions are precise, timely, and aligned with application needs, effectively balancing performance and cost-efficiency.

Example of Autoscaling in Action

Without autoscaling, the platform risks frustrating users who face login delays or failures, interrupted streaming that leads to customer churn and negative feedback, and missed opportunities to retain viewers by engaging them with personalized recommendations.

How Autoscaling addresses the problem

This solution works by prioritizing critical services like login and streaming, ensuring users can access their accounts and enjoy smooth playback without interruptions. Autoscaling optimizes resource usage by dynamically allocating additional resources only when needed, avoiding over-provisioning and reducing costs during off-peak times. It combines proactive scaling, which anticipates predictable surges such as a show’s release time, with reactive scaling to adjust for real-time spikes in demand.

Additionally, by scaling the Recommendation Service, the platform keeps users engaged with relevant content, enhancing retention and overall user satisfaction.

Monitoring in Autoscaling

Monitoring is a critical component of any autoscaling system, providing the data necessary to make informed and timely scaling decisions. By continuously tracking the health, performance, and usage of infrastructure, monitoring ensures that resources are allocated effectively to meet application demand. Without robust monitoring, autoscaling systems would operate blindly, risking overprovisioning, underperformance, or even complete service outages. Real-time metrics help identify sudden spikes in demand, enabling the system to scale up resources before performance issues occur. Monitoring also prevents overprovisioning by analyzing usage patterns and scaling down resources during low-traffic periods, reducing costs while maintaining availability. By continuously tracking system performance, monitoring detects and resolves bottlenecks or anomalies early, ensuring stable operations. Additionally, historical data from monitoring allows the system to predict recurring traffic patterns, like daily peak hours, and adjust capacity proactively to handle demand effectively.

Key Metrics to Monitor

Monitoring focuses on collecting and analyzing specific metrics that reflect the system’s health and workload. These include:

• CPU Utilization: A primary indicator of processing demand. High CPU usage often signals the need for scaling out additional instances or scaling up the current capacity.
• Memory Usage: Ensures that applications have sufficient memory to handle operations without slowing down or crashing. Memory bottlenecks often correlate with spikes in user activity.
• Request Latency: Measures the time taken to respond to user requests. An increase in latency can indicate that the system is under strain and requires additional resources.
• Request Throughput: Tracks the number of incoming requests per second, providing a clear view of workload trends and traffic surges.
• Disk I/O and Network Bandwidth: Particularly important for data-heavy applications, these metrics help monitor resource strain during high read/write or data transfer operations.

Challenges in Autoscaling

• Scaling Latency: Provisioning new resources can take time, especially in virtualized environments, leading to temporary performance degradation during sudden traffic spikes.
• Tuning Metric Thresholds:
  • Overly aggressive thresholds can cause frequent scaling, increasing costs and destabilizing the system.
  • Overly conservative thresholds may fail to meet demand during peak loads.
• Service Interdependencies: Scaling one service without accounting for dependent services (e.g., scaling an API without adjusting the backend database) can create bottlenecks and degrade overall system performance.
• Unexpected Costs: Dynamic scaling with no upper limits can lead to rapidly increasing expenses during high-traffic periods.
• Challenges with Stateful Systems: Databases and other stateful components require careful synchronization and orchestration to maintain consistency across scaled instances.
• Application Design Limitations:
  • Applications must be architected to support scaling, especially horizontal scaling!
  • A microservices-based architecture, stateless components, and decoupled services are essential for seamless scaling.
  • Failure to incorporate these elements can result in increased complexity, data inconsistencies, and reduced performance.
• Architectural Planning: Scalability must be considered from the early development stages to ensure the application can efficiently adapt to varying loads and grow alongside demand.

At Tempico Labs, we help to navigate the challenges of autoscaling, from optimizing metric thresholds and reducing scaling delays to managing service dependencies and guiding through decoupling roadmaps. Our expertise in designing scalable architectures and scaling stateful systems ensures data consistency while maintaining performance, fast time to market; and keeping costs reasonable.