AS Advantages

Automatic Resource Scaling

AS adds ECS instances and increases bandwidths for your applications when the access volume increases and removes unneeded resources when the access volume drops, ensuring system stability and availability.

• Scaling ECS Instances on Demand

  AS scales ECS instances for applications based on demand, improving cost management. ECS instances can be scaled dynamically, on a schedule, or manually:

  • Dynamic scaling

    Dynamic scaling allows scale resources in response to changing demand using alarm-based policies.

  • Scheduled scaling

    Scheduled scaling helps you set up your scaling schedule according to predictable load changes by creating periodic or scheduled policies.

  • Manual scaling

    You can either manually change the expected number of instances of your AS group, or add or remove instances to or from the AS group.

  Consider a train ticket booking application running on the public cloud. The load of the application may be relatively low during Q2 and Q3 because there are not many travelers, but relatively high during Q1 and Q4. Traditionally, there are two ways to plan for these changes in load. The first option is to provide enough servers so that the application always has enough capacity to meet demand, as shown in Figure 1. The second option is to provision servers according to the average load of the application, as shown in Figure 2. However, these two options may waste resources or be unable to meet demand during peak seasons. By enabling AS for this application, you have a third option available. AS helps you scale servers to keep up with changes in demand. This allows the application to maintain steady, predictable performance without wasting money on any unnecessary resources, as shown in Figure 3.

  Figure 1 Over-provisioned capacity
  
  Figure 2 Insufficient capacity
  
  Figure 3 Auto-scaled capacity
  
• Scaling Bandwidth on Demand

  AS adjusts bandwidth for an application based on demand, reducing bandwidth costs.

  There are three types of scaling policies you can use to adjust the IP bandwidth on demand:

  • Alarm-based policies

    You can configure triggers based on metrics such as outbound traffic and bandwidth. When the system detects that the triggering conditions are met, the system automatically adjusts the bandwidth.

  • Scheduled policies

    The system automatically increases, decreases, or adjusts the bandwidth to a fixed value on a fixed schedule.

  • Periodic policies

    The system periodically adjusts the bandwidth based on a configured periodic cycle.

  For example, you can use an alarm-based policy to regulate the bandwidth for a livestreaming website.

  For a livestreaming website, service load is difficult to predict. In this example, the bandwidth needs to be dynamically adjusted between 10 Mbit/s and 30 Mbit/s based on metrics such as outbound traffic and inbound traffic. AS can automatically adjust the bandwidth to meet requirements. You just need to select the relevant EIP and create two alarm policies. One policy is to increase the bandwidth by 2 Mbit/s when the outbound traffic is greater than X bytes, with the limit set to 30 Mbit/s. The other policy is to decrease the bandwidth by 2 Mbit/s when the outbound traffic is less than X bytes, with the limit set to 10 Mbit/s.

• Evenly Distributing Instances by AZ

  To reduce the impact of power or network outage on system stability, AS attempts to distribute ECS instances evenly across the AZs that are used by an AS group.

  A region is a geographic area where resources used by ECS instances are located. Each region contains multiple AZs where resources use independent power supplies and networks. AZs are physically isolated from one another but interconnected through an intranet. AZs are engineered to be isolated from failures in other AZs. They provide cost-effective, low-latency network connections to other AZs in the same region.

  An AS group can contain ECS instances in one or more AZs within a region. When scaling the capacity of an AS group, AS attempts to evenly distribute ECS instances across AZs used by the AS group based on the following rules:

  Evenly distributing new instances to balanced AZs

  AS attempts to evenly distribute ECS instances across the AZs used by an AS group. To do it, AS adds new instances to the AZ with the fewest instances.

  Consider an AS group containing four instances that are evenly distributed in the two AZs used by the AS group. If a scaling action is triggered to add four more instances to the AS group, AS adds two to each AZ.

  Figure 4 Evenly distributing instances
  

  Re-balancing instances across AZs

  After you have manually added or removed instances to or from an AS group, the AS group can become unbalanced between AZs. AS compensates by re-balancing the AZs during the next scaling action.

  Consider an AS group containing three instances that are distributed in AZ 1 and AZ 2, with two in AZ 1 and one in AZ 2. If a scaling action is triggered to add five more instances to the AS group, AS adds two to AZ 1 and three to AZ 2.

  Figure 5 Re-balancing instances
  

Enhanced Cost Management

AS enables you to use ECS instances and bandwidths on demand by automatically scaling resources for your applications, eliminating waste of resources and reducing costs.

Higher Availability

AS ensures that you always have the right amount of resources available to handle the fluctuating load of your applications.

Using ELB with AS

Working with ELB, AS automatically scales ECS instances based on changes in demand while ensuring that the load of all the instances in an AS group stays balanced.

After ELB is enabled for an AS group, AS automatically associates a load balancing listener with any instances added to the AS group. Then, ELB automatically distributes traffic to all healthy instances in the AS group through the listener, which improves system availability. If the instances in the AS group are running a range of different types of applications, you can bind multiple load balancing listeners to the AS group to listen to each of these applications, improving service scalability.

High Fault Tolerance

AS monitors instances in an AS group, and replaces any unhealthy instances it detects with new ones.