If an organization wants to expand significantly, its websites, applications, or other online platforms must be able to handle the increased traffic and usage.
Every application obtains the computing power it requires from a server or group of servers, also known as a server farm, on which the application is hosted. Each server has a limited amount of computing power. So, what happens when the app requires more processing power than is currently available? You autoscale.
Autoscaling saves you the time and effort required to manually scale a server or system to meet all potential levels of server load, whether high or low.
What is autoscaling?
Autoscaling is a way to automatically scale the computing resources of your application based on the load on a server farm. It involves scaling up the resources when there is a spike or rise in web traffic and scaling down when traffic levels are low.
Automatic scaling is widely accepted for its versatility, flexibility, and cost-effectiveness. Some of the world’s most popular websites, such as Netflix, have opted for autoscaling support to meet the growing and ever-changing consumer needs and demands.
Amazon Web Services (AWS), Microsoft Azure, and Oracle Cloud are some of the most popular cloud computing vendors offering autoscaling services.
Why is autoscaling important?
Autoscaling is especially relevant today as the world is committing to reduce carbon emissions and their footprint on the planet. The process helps conserve energy by putting the idle servers to sleep when the load is low.
Autoscaling is most beneficial for applications where the load is unpredictable because it promotes better server uptime and utilization. Based on the conditions specified by the system administrator, autoscaling can automatically couple or uncouple from a computing matrix to adjust to the load. This saves electricity and usage bills since many cloud service providers charge based on server usage.
Some of the other benefits of autoscaling are:
- Better load management: Autoscaling supports effective server load management since servers can be used during low traffic to complete non-time-sensitive computing tasks. This is possible as autoscaling frees up significant server space with less traffic.
- Dependability: When server loads are highly erratic and unpredictable, such as for e-commerce websites or video streaming services, autoscaling prepares the server to handle the varying server demands, making it a dependable option.
- Fewer failures: Autoscaling services ensure that server failure instances are immediately replaced with another optimal server. This reduces application downtime.
- Lower energy consumption: With the application of autoscaling to a website, servers will be able to go to sleep during periods of low traffic. This significantly lowers the amount of electricity that a company uses in cases where its website is hosted on its own server infrastructure.
- Cost-effective: Most cloud computing service providers charge based on server usage and not capacity, which translates to lower server costs when compared to paying for the maximum required capacity irrespective of usage. This is particularly beneficial for organizations that see massive fluctuations in web traffic, such as online retail outlets, travel booking applications during holiday seasons, and so on.
How autoscaling works
A server cluster comprises the main servers and replicated servers made available when traffic spikes. When a user initiates a request, it passes over the internet to a load balancer that communicates to the servers whether to scale up or out its supplementary units.
In fact, the entire process of autoscaling banks on load balancing – it defines the server pool’s efficiency in handling traffic.
Types of autoscaling
Based on how servers are called from the circuit, there are three major types of autoscaling.
Reactive autoscaling
Reactive autoscaling bases its operation on preset “triggers” or thresholds specified by the administrator, which activates additional servers when crossed. Thresholds can be set for key server performance metrics such as the percentage occupied capacity. For example, reactive autoscaling happens when additional servers are set to kick in when the main server runs at 80% capacity for a full minute.
Essentially, this type of autoscaling “reacts” to incoming traffic.
Proactive or predictive autoscaling
Suitable for applications where server loads are more or less predictable. Predictive or proactive autoscaling schedules additional servers to kick in automatically during peak traffic times based on the time of day. This type of autoscaling uses artificial intelligence (AI) to “predict” when traffic would be high and schedules server augmentations in advance.
Scheduled autoscaling
Scheduled autoscaling is similar to predictive autoscaling; the only difference is in scheduling additional servers for peak time. While predictive autoscaling does this autonomously, scheduled autoscaling relies more on human input to schedule the servers.
The key benefits of autoscaling – why it’s an attractive option
Any business, large or small, can reap the various benefits of autoscaling services. Below are some of the key advantages of autoscaling:
Lower energy consumption
Applying autoscaling to a website puts servers to sleep during periods of low traffic. This significantly lowers a company’s power consumption when applications are hosted on its in-house server infrastructure.
Cost-effectiveness
Most cloud computing service providers charge based on server usage, not capacity. This translates to lower server costs compared to paying for the maximum required capacity regardless of usage. Organizations with massive fluctuations in web traffic, such as online retail stores and travel booking applications during the holiday season, benefit greatly from reduced server costs.
Better load management
Autoscaling supports effective server load management since servers can be used during periods of low traffic to complete computing tasks that are not time-sensitive. This is possible since autoscaling frees up significant server space when there is less traffic.
Protection from website or app failures
Autoscaling services such as those from AWS ensure prompt replacement of faulty instances. This offers an app considerably limited protection against network, application, and hardware failures.
Dependability
When server loads are highly erratic and unpredictable, such as in e-commerce websites or video streaming services, autoscaling ensures preparedness to handle the varying server demands, making it a dependable option. Server failure is often a costly affair capable of causing tremendous losses to the organization. In 2018, J.Crew’s server failure on Black Friday cost them a whopping $700,000 in sales. Reliance Jio in India suffered a similar outage in 2022, as a result, many Jio subscribers were unable to send or receive SMS messages or make or receive phone calls.
The overall advantage of auto scaling is that it eliminates the need to manually respond in real-time to traffic spikes that necessitate new resources and instances by changing the active number of servers. Each of these servers needs to be configured, monitored, and decommissioned, which is the core of autoscaling.
Autoscaling in practice
Various cloud service providers deploy autoscaling through indigenously developed processes or software that help optimize server performance. Let’s look at some of these examples in detail.
AWS autoscaling
Amazon Web Services (AWS) sports multiple services for autoscaling: AWS service and Amazon EC2. Amazon EC2 relies on launch templates to derive information about launching instances (like VPC subnet). Users have the option to set the instance count manually or let EC2 do it automatically.
Google Compute Engine (GCE)
GCE enables autoscaling via Managed Instance Groups (MIGs). Its console gives users the freedom to define MIGs, organize them according to the desired performance metric (such as CPU utilization), adjust them for the required autoscaling cap, and activate autoscaling with a click of a button.
IBM Cloud
IBM’s services work on virtual servers autoscaled through an implement called cluster-autoscaler. Nodes are kicked in or out based on the instance load when the preset threshold is exceeded. This autoscaling mechanism works with workload policies that users define as per sizing needs.
Microsoft Azure
Azure provides its users a console to set autoscale programs. They can just navigate to the autoscale option on their console, add new settings and rules for scaling on various server parameters, and set the conditions for autoscaling.
Oracle Cloud Infrastructure
Oracle Cloud provides full-scale control over autoscaling. It allows users to configure it for metric-based or schedule-based autoscaling. Users can edit and configure autoscaling policies. Oracle offers multiple autoscaling services to elastically balance network load on servers.
Autoscaling is not as easy as it sounds
Today, autoscaling is a powerful, sophisticated, and useful computing feature that helps millions of websites or apps manage their server loads. However, like traditional scaling, you need to overcome many hurdles to achieve autoscaling. Here are four overarching reasons why autoscaling can be difficult to optimize and apply, especially on large servers with massive amounts of information.
1. Searching for information becomes difficult
Imagine an e-commerce website with a database of over a million names and customer contacts. Regardless of the site’s measures to organize this massive data, scouring it for information is not an easy task. With autoscaling, however, this information needs to be made available at all times across the additional servers – a significant problem to address.
2. Consistency is hard to achieve
When an e-commerce website opts for autoscaling services, another major hurdle is achieving consistency. For example, during flash sales, product availability data is constantly updated. These changes should be made available to all users on the platform to ensure that no one can place an order for a product no longer available. Ensuring the consistency of information and data in such situations, especially when the server load is high, isn’t simple.
3. Concurrent use increases server demands
Using the same example above, suppose millions of users are trying to log into the e-commerce website to purchase the same product. Although unlikely, this is the kind of situation a server should be ready for. Each of these users requires simultaneous access to the data and information on the servers. This is a major challenge any autoscaling attempt must overcome.
4. Speed maintenance becomes complex
When it comes to large amounts of information, scaling up or adding a server inevitably affects the speed at which these computing resources can be deployed to provide information to users on the application or website.
Native autoscaling support issues
Apart from the sheer amount of computing resources and expertise required to tackle the challenges of autoscaling and provide a satisfying customer experience, most cloud service providers don’t offer native autoscaling support because the associated costs are very high.
Physical server costs
Cloud-based hosting services that offer autoscaling almost always use horizontal autoscaling to achieve the desired result. This entails deploying additional servers or machines to the existing resource pool versus vertical autoscaling that involves upgrading the existing servers and machines. A good example of vertical autoscaling is increasing RAM capacity in an existing machine. Regardless of the means used to achieve autoscaling, a cloud service provider’s expenses are high.
Investment in your workforce
To ensure the efficient management of autoscaling, a dedicated team of experts is needed to oversee and monitor the process, especially for high-traffic websites. For example, in the United States, e-commerce websites such as Shopify or Amazon see more sales during the festive sale season than on Black Friday. Therefore, not all cloud service providers are willing to make the substantial investments required to build a team capable of supporting autoscaling natively on their platforms.
Autoscaling is here to stay
Autoscaling is an increasingly popular web hosting feature that’s undoubtedly here to stay. With enormous dedication and financial backing to the technology and strategy, many tech giants have ensured that consumers now have access to reliable autoscaling features and constantly improve their customer experience.
Organizations interested in making the most of autoscaling can choose either vertical or horizontal autoscaling options. Right off the bat, vertical autoscaling isn’t ideal for web applications or resources with thousands of users since there are several architectural limitations to upgrading existing servers that affect availability.
On the other hand, horizontal autoscaling ensures continued availability. This is because user sessions aren’t restricted to a localized server but seamlessly spread out across a server pool that can be expanded or contracted based on the web application’s ever-changing needs.
Despite the investment required and challenges involved, autoscaling offers organizations several short-term and long-term benefits. Therefore, if an organization wants to scale its operations and web resources, autoscaling is often the best option available.
Achieving high availability for your application can be a chore. Read more about high availability options so you can spot any errors and traffic movements in your application and take optimal action.
1. What are the types of Autoscaling?
Autoscaling is classified into four types: manual scaling, scheduled scaling, dynamic scaling, and predictive scaling.
2. What is Autoscaling in Devops?
Auto scaling ensures that your application always has the compute capacity it requires and eliminates the need to manually monitor server capacity. You can autoscale based on incoming requests (front-end) or the number of jobs in the queue as well as the length of time jobs have been in the queue (back-end).
3. What exactly is the distinction between Automatic scaling and load balancing?
While load balancing will re-route connections from unhealthy instances, new instances must be added to the network. As a result, auto scaling will start these new instances, and load balancing will connect to them.
4. What is microservices scalability?
Scalability is the most important feature of microservices. Statically, monolithic programmes share the resources of the same machine. Microservices, in turn, scale their specifications as needed. Microservices architectures can then manage their resources and allocate them where and when they are needed.
