Mechanisms for Autoscaling

Explore how to monitor CPU, memory, I/O, and network load indicators to implement autoscaling in distributed microservices applications. Understand how these metrics impact performance and how to adjust configurations to maintain uptime and responsiveness in cloud environments.

We'll cover the following...

Compute and CPU load
Disk and I/O load
Request and network load
Memory-intensive operations

There are four primary indicators to watch during the operation of an application that can indicate potential issues with maintaining uptime or responsiveness SLAs. These include CPU load, I/O load (often seen as disk pressure in Kubernetes), request and network load (often seen as network pressure in Kubernetes), and memory load. Understanding these indicators is essential in helping to prepare us to adjust configuration settings, therefore leading to scalable supporting infrastructure. Understanding how our application components affect each of these indicators is important as well. ...

1.Introduction

2.The Sample Application

3.The Producer-Consumer Pattern

4.Message Brokers

5.Domain Model and Asynchronous Events

6.Containerization and Local Environment Setup

7.Localized Testing and Debugging of Microservices

8.Microservice Observability

9.CI/CD Pipelines and Integrated Testing

10.Fault Injection and Chaos Testing

11.Modern Design Patterns for Scalability

12.Minimizing Data Loss

13.Service and Application Resiliency

14.Telemetry Capture and Integration

15.Observability Revisited

16.Conclusion

Mechanisms for Autoscaling