The hype is reasonable, as setting the right auto-scaling strategy with your cloud applications can save you tons of money. Are you sick of hectic manual resource scaling strategies or looking for futuristic trends in cloud resource scaling? You are at the right place. This article will guide you to save yourself from paying for the cloud resources your applications rarely utilize. So, let’s dive in! Cloud computing provides on-demand different computing and IT resources and services over the internet with minimal management efforts. Scalability means increasing or decreasing these cloud resources to adapt to the changing needs of the application.
Scaling Strategies
A system can grow or shrink its resources in the existing infrastructure with two different strategies:
Vertical Scaling Horizontal Scaling
Vertical scaling
Vertical scaling is upgrading or downgrading the existing resources, instances, or nodes of the existing infrastructure. For example, a system adds more computing power to the existing nodes in vertical scaling. Verticle scaling has two operations: scale-up and scale-down. Adding more power or resources to the existing nodes is a scale-up operation. While removing some resources from the existing nodes is a scaled-down operation.
Horizontal scaling
Unlike verticle scaling, horizontal scaling refers to adding or removing more instances or nodes from the existing infrastructure instead of upgradation of the existing nodes. In horizontal scaling, a system grows by adding more nodes or machines to the existing infrastructure. Horizontal scaling has two operations: scale-out and scale-in. Scale-out means adding more nodes or machines to existing infrastructure. Conversely, the scale-in operation removes any existing node or machine from the existing infrastructure.
What is Auto-scaling in Cloud Computing?
Auto-Scaling is a cloud computing jargon referring to automatically adjusting the cloud resources for an application. It is the ability to increase or decrease resources automatically without any human interaction to maintain the performance of the applications. Auto-scaling has potential applications everywhere, from your web application to databases. It can also help your company manage seasonal traffic spikes and sudden surges in demand. For example, if you’re expecting an increase in sales around the holidays, your auto-scaling strategy could automatically add (cloud) servers to help you cope with the increased traffic bursts.
Why is Auto-scaling Important to Grow Your Bussiness
As your business grows, you may find that you need to expand your engineering team to meet demand. This can be challenging because finding engineers skilled in the right technologies can be difficult. In addition, hiring engineers is a lengthy and expensive process, and there may be times when you need them right away but don’t have the budget to pay them. Auto-scaling allows you to scale up your servers as needed while avoiding the expense of hiring more engineers. You still have full control of your infrastructure, but you can scale up and down using predefined rules instead of manually adding servers. This saves your engineering team the time and effort it takes to manually add servers, especially if you urgently need more servers. Auto-scaling also removes the responsibility of manually adding and maintaining servers from your engineers, which means they can focus on other tasks.
Who Needs Auto-scaling
Auto-scaling is an excellent tool for businesses that rely heavily on their applications. Auto-scaling can help you save money, optimize resources, and ensure your application is always running optimally. If your application needs more computing power, auto-scaling can automatically scale up the resources to meet the demand. If the demand decreases, auto-scaling can automatically scale the resources down to conserve energy and costs. Auto-scaling is also helpful for businesses that need to improve the availability of their applications. By adding additional servers to take over in the event of a failure, you can ensure your application is always available. This is especially important for businesses that rely heavily on their applications.
When Not to Use Auto-scaling
Auto-scaling quickly scales resources up or down to meet the demands of the applications and improves their availability. However, auto-scaling is not always the right choice. Auto-scaling may be unnecessary if your application has low or infrequent usage. In this case, you may better use a static approach to scaling your resources. You should also consider static scaling over auto-scaling if your application has predictable usage patterns. Finally, you should consider the complexity of auto-scaling. Auto-scaling can be complex and require a lot of tuning and troubleshooting. If you don’t have the time or resources to dedicate to this, you may want to consider a static approach to scaling your resources.
Different Approaches for Auto-scaling
Auto-scaling is classified into several approaches based on the triggering mechanisms for auto-scaling decisions. The auto-scaling decision includes scale-up or scale-down operations if you are using vertical scaling and scale-out or scale-in operations while using horizontal scaling. Let’s briefly have a look at the three most common classifications for auto-scaling strategies:
#1. Reactive or demand-driven auto-scaling
An auto-scaling method that triggers the auto-scaling decision (growth or shrink of infrastructure) as a reaction to an event occurring. Generally, this type of auto-scaling happens when a system detects an increase in demand. The increase in demand can be tied up with real-time monitoring of already available infrastructure resources. For example, a system can grow the infrastructure whenever the CPU utilization of already available nodes exceeds a threshold. Similarly, the resource shrinks based on CPU under-utilization thresholds.
#2. Scheduled or time-driven auto-scaling
Scheduled auto-scaling methods grow or shrink the infrastructure according to the pre-defined scheduled time. This auto-scaling method considers the fixed time intervals to add or remove the resources.
#3. Predictive Auto-scaling
This auto-scaling method automatically adjusts the resources of an application to meet the projected demand. Predictive auto-scaling uses machine learning to forecast demand and growth or shrink the resources according to the projected demand. The predictive approach is designed to anticipate and plan for future incoming workloads. It combines past trends with current metrics and predicts how the application will perform and what resources it will require to sustain that performance level.
How Does the Predictive Auto-scaling Work?
It monitors resource utilization and analyses historical data to predict future demand. Resource utilization refers to metrics such as CPU and memory usage. Predictive auto-scaling uses trending machine learning methods to predict demand, and these methods train over historical data. Predictive auto-scaling models can analyze factors such as the time of day, day of the week, and the number of customers online to forecast future demand. When you can forecast potential demand, you can set thresholds accordingly. With the latest trends in Machine Learning, predictive auto-scaling has expanded its scope from predicting future demands. Re-enforcement and sequential learning approaches have made it possible to learn from mistakes continuously. Therefore, predictive algorithms can train over new events and adjust thresholds accordingly.
Benefits of Predictive Auto-scaling 👍
Predictive auto-scaling is capable of scaling an application more quickly and accurately. Another advantage of predictive auto-scaling is that it’s more proactive than reactive auto-scaling. Consequently, predictive auto-scaling better manages the load on an application. Predictive auto-scaling can also be more accurate than reactive because it analyzes historical data to forecast future demand. It is usually more precise than reactive auto-scaling in managing resources. Some other benefits of predictive auto-scaling are as follows:
Requires little to no manual intervention Easier to scale and add instances as the load increases Reduces chances of over-provisioning ensures availability by pro-actively reacting to predicted demands
Disadvantages of Predictive Auto-scaling 👎
Some drawbacks to a predictive auto-scaling strategy are as follows:
Challenging to choose the right predictive algorithm Poorly pre-processed training data can result in high false-positive predictions
Why Use Predictive Auto-scaling?
Auto-scaling can be a very manual process and can require frequent attention depending on the strategy you use. Predictive auto-scaling can help to automate much of that process and make it less necessary for you to make adjustments manually. Auto-scaling strategies can require the application to be either over-provisioned or under-provisioned. Over-provisioning can add unnecessary expense to your application. Under-provisioning can create bottlenecks and result in outages for your application. Most modern applications make use of load balancers. Predictive auto-scaling can help to use this load balancer optimally by shifting instances between servers based on actual metrics and performance instead of just the number of requests.
When to Use Predictive Auto-scaling Strategy?
A predictive auto-scaling strategy might be a good choice for your application if you want to reduce the manual intervention required to adjust the number of instances. If your application serves a general group of customers or visitors, you may want to use a more reactive monitoring and scaling strategy. If your application is for something with a set timeframe for the customer, you may want to use a more predictive strategy.
Where to Find Auto-scaling Services?
You can also use third-party services to help you with auto-scaling. Services such as RightScale, Scalr, and AppFormix offer a range of auto-scaling services, such as predictive analytics, reactive auto-scaling, and hybrid auto-scaling. Finally, you can use open-source tools to help you with auto-scaling. Tools such as Kubernetes and Apache Mesos can help you quickly and easily set up auto-scaling for your applications.
Conclusion
Auto-scaling is an important part of building a resilient and reliable application. Predictive auto-scaling is one potential strategy you can use for your application. If your application uses a load balancer, it’s important to use this auto-scaling effectively to avoid unnecessary costs and potential outages. Predictive auto-scaling can help to use the load balancer optimally based on current metrics and performance rather than just the number of requests. Predictive auto-scaling is helpful because it can be used to plan for future growth and proactively adjust resources. It is not easy to design and implement, but it can be helpful if done correctly. Predictive auto-scaling can be a good choice for your application if you want to reduce the manual intervention required to adjust the number of instances.
