IoT Platform Scaling

The IoT platform built on modern technologies such as Ruby on Rails, PostgreSQL, GO, Docker and KVM already offers high performance and flexibility. However, as the number of users, data and devices grows, there is a need to scale the system.

IoT Platform Scaling Strategies

flowchart LR
subgraph subGraph0["IoT Platform Scaling Strategies"]
F["Vertical Scaling"]
A["Application Servers (Ruby on Rails)"]
G["Horizontal Scaling"]
B["Data Collection Servers (GO)"]
C["PostgreSQL Database"]
H["Database Optimization"]
D["Docker/Kubernetes"]
E["Frontend Scaling"]
end
A --> F & G
B --> F & G
C --> F & G & H
D --> B & A
E -- Efficient_Distribution --> D

Vertical and horizontal zooming

Vertical Scaling

• Server Resource Upgrade:

  • Adding CPU, RAM, and SSD for application servers (Ruby on Rails) and data collection servers (GO).
  • Increased PostgreSQL DBMS performance with more powerful disk arrays and index optimization.

• Benefits: Simplicity of implementation, minimal changes to architecture.

• Limitations: Physical limitations of servers, high cost.

Horizontal scaling.

• Add new servers:
  • Ruby on Rails: Deploy additional application instances through load balancing (e.g., using Nginx or HAProxy).
  • GO servers: Easily scale due to multi-threading and support for horizontal scaling.
  • PostgreSQL: Data replication and sharding for load balancing.
• Benefits: High fault tolerance, infinite scalability.
• Limitations: Complexity of managing a distributed system.

Using Kubernetes/Docker-Compose for container orchestration

Migration to Kubernetes/Docker-Compose

• Application Containerization:
  • Ruby on Rails and GO servers are packaged into Docker containers.
  • Kubernetes/Docker-Compose manages deployment, scaling and monitoring of containers.
• Automatic Scaling:
  • Horizontal Pod Autoscaler (HPA) automatically increases the number of Pods (Pods) based on load.
  • Cluster Autoscaler adds new nodes to the cluster when resources become scarce.
• Benefits: High flexibility, automation, fault tolerance.

Example architecture on Kubernetes

• Ingress Controller: For routing requests to Ruby on Rails and GO servers.
• StatefulSets: For stateful PostgreSQL replica management.
• Service Mesh: Using Istio or Linkerd to manage traffic and improve security.

Database Optimization

PostgreSQL Replication and Sharding

• Replication:
  • Configuring master-slave replication to distribute the read load.
  • Using tools like Patroni to automatically manage replication.
• Sharding:
  • Splitting data across multiple PostgreSQL clusters to distribute the write load.
  • Using Citus to horizontally scale PostgreSQL.

Caching

• Redis or Memcached:
  • Caching frequently requested data to reduce database load.
  • Integration with Ruby on Rails via ActiveSupport::Cache.

Frontend Scaling

• Client-side caching:

  • Using Service Workers and Cache API to cache static resources.

• CDN (Content Delivery Network):

  • Distributing static files via CDN to reduce the load on servers.

Advantages of the proposed solution

• High fault tolerance: Thanks to Kubernetes/Docker-Compose and PostgreSQL replication.
• Flexibility: Easy addition of new servers and microservices.
• Automation: Minimal human intervention in scaling processes.
• Cost-effectiveness: Optimization of resource usage due to automatic scaling.

The described scaling scenario allows the telemetry system to effectively cope with growing loads, providing high performance, fault tolerance and flexibility. The implementation of modern technologies makes the IoT platform ready for future challenges.