Auto Scaling

Auto Scaling ability refers to dynamically adjusting resources (such as computing resources, storage resources, bandwidth, etc.) based on actual needs to meet business requirements under different workloads. The system can automatically increase resources during peak periods and release resources during off-peak periods to improve system stability and performance by Auto Scaling.

Auto Scaling is an important component of business stability solutions. It can be applied to various systems, including cloud computing environments, web applications, and databases. The main purpose of Auto Scaling is to provide reliable system performance and ensure the effective utilization of resources under both high and low workloads.

The characteristics and implementation plan of Auto Scaling are as follows:

Characteristics:

1. Automation: Auto Scaling is an automated process that does not require manual intervention. The system automatically adjusts resources based on preset rules and policies.

2. Real-time response: Auto Scaling can quickly adjust resources based on real-time workload conditions. When the workload increases, the system automatically increases resources to meet the demand. When the workload decreases, the system automatically releases excess resources to save costs.

3. Flexibility: Auto Scaling can be configured based on different needs and rules. It can be triggered by different metrics such as CPU usage, memory utilization, network bandwidth, or custom business metrics (QPS, RT), etc.

Implementation Plan:

1. Monitoring and measurement: Monitoring and measuring the system's workload and resources is the first step. Collect key performance indicators and data. Monitoring tools and metric systems can be used to achieve this.

2. Rules and policies: Based on monitoring data, develop rules and policies for Auto Scaling. These rules can include resource thresholds, trigger conditions, scaling strategies, and more.

3. Automatic scalability: Configure automated scaling mechanisms based on the rules and policies. This can be achieved through cloud service providers' automatic scaling functions, automation scripts, or container orchestration tools.

4. Monitoring and optimization: Regularly monitor and evaluate the effectiveness of Auto Scaling and optimize it. Adjust and optimize the scaling rules and policies based on actual conditions to achieve better performance and cost-effectiveness.

Alibaba Cloud's Elastic Scaling Service (ESS) has the advantages of automation, cost reduction, high availability, flexibility, intelligence, and ease of auditing. Through simple operations, various scaling modes can be configured to implement automated scaling mechanisms based on business scenarios. This enables the system to quickly respond to workload changes, automatically adjust resources based on demand, and provide better user experience and service quality.

There are three methods for traditional instance management, t: fixed number of instances, HPA (Horizontal Pod Autoscaler), and CronHPA. Each method has its own drawbacks and limitations, such as resource waste, delayed elasticity, and the need to adjust scheduling strategies on business demands, resulting in poor feasibility. Especially for scenarios with extremely high requirements for timeliness, more accurate and rapid startup capabilities are needed. In this case, more advanced capabilities such as serverless and AHPA must be taken into consideration.

Serverless container running mode provides Elastic Container Instance (ECI) with elastic scheduling capability, which realizes extreme elasticity, capacity planning-free, on-demand usage, and pay-as-you-go capabilities. It can significantly improve the efficiency of application deployment for scenarios such as job tasks, CI/CD, Spark big data computing, and elastic online applications, and reduce computing costs.

Traditional HPA (Horizontal Pod Autoscaler) strategies start scaling up the resources after the business volume increases, which may result in delayed provision of resources to meet the business's needs. AHPA (Advanced Horizontal Pod Autoscaler) can automatically identify the elasticity period based on historical business metrics, predict capacity requirements in advance, and solve the problem of delayed elasticity. It achieves faster (millisecond-level prediction, second-level elasticity), more stable (combining active prediction and passive prediction), and more accurate (supporting minute-level boundary protection configuration) scalability.