3.6 KiB
Scalability and Load Balancing Design
Scenario
Design an architecture capable of handling:
- 15,000 requests per second (req/s)
Each application container can process:
- 500 req/s
A 30% capacity buffer is required to handle traffic spikes and unexpected load.
1. High-Level Architecture
The proposed architecture consists of the following components:
- DNS
- Load Balancer
- Multiple API containers
- Auto Scaling service
- Container Orchestrator
- Database
- Ghaymah Block Storage
- Monitoring and Alerting
Architecture Diagram
The following diagram illustrates the proposed scalable architecture for handling 15,000 requests per second using load balancing, auto scaling, centralized monitoring, and persistent storage.
Request Flow
Users
│
▼
DNS
│
▼
Load Balancer
│
▼
API Containers
│
▼
Database
Monitoring components (Prometheus, Grafana, and centralized logging) collect metrics and logs from the Load Balancer, API containers, and database.
2. Container Capacity Calculation
Step 1: Calculate Required Containers
Traffic:
15,000 req/s
Container capacity:
500 req/s
Required containers:
15000 / 500 = 30 containers
Step 2: Add 30% Safety Buffer
30 × 1.30 = 39 containers
Recommended Deployment Size
Minimum recommended deployment:
39 containers
This provides additional capacity for traffic spikes and reduces the risk of resource saturation during unexpected load increases.
3. Cold Start Strategy
To minimize startup delays during scaling events, the following strategies are recommended.
Warm Pool
Maintain 2–3 idle containers ready to receive traffic immediately.
Container Image Optimization
- Use lightweight base images.
- Remove unnecessary packages and dependencies.
- Keep image size as small as possible.
Image Pre-Pulling
Pre-pull container images on worker nodes to reduce deployment time and avoid downloading images during scale-out events.
Health Checks
New containers must pass readiness and liveness checks before receiving production traffic.
Gradual Traffic Shift
The Load Balancer should gradually route traffic to newly started containers after successful health verification.
Predictive Scaling
Scale proactively based on CPU utilization, memory usage, and request-rate trends instead of waiting until the system reaches full capacity.
4. Using Ghaymah Block Storage for Stateful Workloads
Ghaymah Block Storage provides persistent storage volumes that remain available even if containers are restarted, replaced, or rescheduled.
This makes it suitable for workloads that require durable data storage.
Examples include:
- Databases
- Message queues
- File storage
- Application uploads
- Persistent logs
Benefits
- Persistent data survives container restarts.
- Volumes can be attached to replacement containers.
- Improves reliability for stateful applications.
- Simplifies backup and disaster recovery.
- Enhances data durability during scaling events.
Best Practices
- Deploy multiple replicas behind a Load Balancer.
- Enable Horizontal Auto Scaling based on CPU, memory, and request rate.
- Configure readiness and liveness probes.
- Use lightweight container images to reduce cold-start time.
- Store persistent data using Ghaymah Block Storage.
- Continuously monitor latency, error rate, CPU, and memory utilization.
- Configure alerts for abnormal response times and infrastructure failures.
- Regularly perform load testing to validate scalability assumptions.