What is an Automated Scaling Listener?
An automated scaling listener is a mechanism that tracks and monitors communications between cloud service users and cloud services, in order to support dynamic scaling. This type of system is typically installed close to the firewall, and constantly monitors data related to the workload.
How Does Automated Scaling Work?
Automated scaling can be used to adjust IT resources according to pre-defined parameters, and send notifications when workloads reach or exceed predetermined thresholds. This gives cloud users the ability to modify their resource allocations as needed.
Example of Automated Scaling
If three users attempt to access the same cloud service, the automated scaling listener will create three duplicate instances of the service. If a fourth user attempts to access the service, the automated scaling listener will reject the fourth attempt and alert the cloud user that the workload limit has been exceeded. In order to increase the limit, the cloud resource administrator must log into the remote administration environment and modify the configuration.
Difference between Auto Scaling vs Load Balancing
Auto Scaling vs Load Balancing: What’s the Difference?
Auto scaling is the process of adding or removing server resources to a system in order to meet demand. Load balancing is the process of distributing traffic across multiple servers to ensure that the application remains available and responsive to user requests. While there are connections between these two concepts, they are distinct processes that can be used independently or together to improve application performance and availability.
How Auto Scaling Works
Auto scaling works by automatically scaling up or down the number of servers in a system based on the current demand. When demand goes up, the system adds more server resources to the system to meet the increased demand. When demand decreases, the system removes server resources to conserve resources. Auto scaling allows applications to scale quickly without manual intervention and ensure that performance is optimized at all times.
How Load Balancing Works
Load balancing is the process of distributing traffic across multiple servers. This helps to ensure that the application remains available and responsive to user requests. Load balancers work by distributing incoming requests across a pool of servers, ensuring that no single server is overloaded. This helps to prevent overloading and reduce the risk of server failure.
Conclusion
Auto scaling and load balancing are both important tools for maintaining performance and availability in applications. While they are distinct processes, they can often be combined to optimize application performance and availability. By using auto scaling and load balancing together, applications can scale quickly and efficiently while ensuring that the application remains available and responsive to user requests.
Difference between Horizontal vs Vertical Auto Scaling
Horizontal Auto Scaling: Achieving Enhanced User Experience
Horizontal auto-scaling is a great way to improve user experience by allowing efficient movement across multiple servers while maintaining single session. This process involves clustering, distributed file systems and load balancing to make sure that the resources are up to the mark. Horizontal auto-scaling does not require downtime as new instances are created independently and improves both performance and availability.
Stateless Servers for Applications with Large Number of Users
Stateless servers are essential for applications that handle large number of users. They make sure that user session does not get bound to a single server and move across multiple servers effortlessly. This helps in scaling incoming requests across instances using elastic load balancing and thus, provides better user experience.
Vertical Auto Scaling: Pros and Cons
Vertical auto-scaling involves scaling by supplying more power rather than more units. It has some architectural issues since it needs to increase the power of an already-running system. Vertical auto-scaling improves performance but not availability and requires downtime for upgrades and reconfigurations. Decoupling application tiers may help in some cases but the best way to handle requests is using stateless servers.