What is a Server? | Definition, Examples, and Key Facts

Traditional servers can run for many years, but web servers in a cloud-native architecture can run for weeks, days, or even less, mainly when using a lightweight operating system like Linux. When a server is viewed as a temporary resource, users can shift their architecture to a more dynamic client-server model capable of handling enormous variations in scale and workflow, particularly with virtualization technologies.

There will be about 1.09 billion websites in 2024, and 252,000 new ones will be created every day, each often relying on its web server hardware. The number of active websites is just under 193 million, many of which are hosted on application servers. A new website is launched every three seconds, and no mail server is immune to high-traffic events.

What is temporary server scaling?

Servers deployed to the public cloud or a data centre using a traditional approach are long-lived compute resources, often referred to as types of servers. However, they need continuous updates, patching, etc. More memory, CPU, and disk space are required to scale a traditional server, but virtual servers can often be adjusted more flexibly.

If you don’t back up these servers consistently, you can lose months of hard work and might not be able to rebuild the server. Temporary scaling does away with configuring a single, long-lived file server. With temporary scaling, one takes advantage of server elasticity: the on-demand growth or reduction of a compute resource for a certain period, often utilizing virtualization techniques.

A high-traffic event is a classic example of how a network can be overwhelmed, especially if the load balancer is not configured to handle multiple application servers efficiently. You can scale the server temporarily when you need to. This is possible with fast shared hosting. According to expert predictions, the shared hosting market will grow at 15% a year and reach $72.2 billion by 2026.

Elasticity vs. scalability

Server elasticity is about pooling, managing, and automating resources as needed. You can do this with any resource: CPU, storage, memory, databases, network bandwidth, web apps, deployment platforms, etc.

Elasticity and scalability are not the same thing. Scalability involves resource growth or shrinkage over time, while elasticity relates to a certain period.

An example is a retailer experiencing a server shortage during the Christmas season. This will cause a number of problems when they try to take online orders with increased web traffic at that specific time of year.

Elasticity is usually managed by using scaling groups. More rarely, it can be achieved manually or through automation scripts that manage the servers’ operating system.

Use of server scaling groups

Scaling groups enable managing servers that perform the same tasks together, such as web servers or database servers. This allows the user to increase or decrease the servers in the group according to the demand for network resources, particularly in environments utilizing virtualization.

You configure these scaling groups with a minimum and maximum threshold for the number of required compute servers, including virtual servers. Then, you assign rules to the group to allow it to increase or decrease based on the current workload of the network.

You add more servers to the group when you need burst capacity. As the capacity requirements decline, the number of servers can be reduced as needed until you reach the minimum threshold.

Distributing requests via load balancing

You usually place a load balancer in front of the group of servers to direct requests to a virtual server within it. Load balancers can assign requests based on a server’s current workload, optimizing resource allocation across multiple types of servers. You assign at least one public domain name to a load balancer, letting it respond to all requests for a web server.  

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