The goal of a web application is to have it automate the things you can’t do. You will have to fix things manually if you are a developer, no matter what. But if something unexpected happens to your application, like if your server is unavailable or slow due to traffic, then you probably want it to handle that problem itself. Load balancing is critical to web performance because of this.
Load balancing is the distribution of network traffic across multiple back-end servers. And a load balancer makes sure that no single server will be overloaded. Because the application load is spread across multiple servers, this increases the responsiveness and consistency of web applications.
As application traffic increases, the load balancer looks at the different servers and picks which ones can best handle the traffic. This maintains a good user experience.
A load balancer will manage the information being sent between a server and the end-user’s device. This server could be on-premise, in a data center, or on a public cloud. Load balancers will also conduct continuous health checks on servers to ensure they can handle requests. If necessary, the load balancer removes unhealthy servers from the server pool until they are restored. Some load balancers even trigger the creation of new virtualized application servers to cope with increased demand, which is an auto-scaling feature.
There are seven layers of networking:
In the seven-layer Open System Interconnection (OSI) model, network firewalls are at levels one to three (L1-Physical Wiring, L2-Data Link, and L3-Network). Meanwhile, load balancing happens between layers four and seven (L4-Transport, and L7-Application)
There are many types of load balancers. You can also think about types of load balancers in terms of the various cloud-based balancers available:
When talking about types of load balancers, it’s also important to note there are hardware load balancers, software load balancers, and virtual load balancers.
Software load balancers have several functions. Not only is it able to act as a catalyst for automation because of it’s predictive analytics that determines bottlenecks before they even happen, but a load balancer can also have other advantages:
Session Persistence is another benefit of load balancers when used right. A browser stores user’s session data as cookies. Session persistence is the ability to make sure all requests for a given session are routed to the same server. If the server changes in the middle of a session, the end user’s data will likely not be saved.
Load balancers rely on algorithms to determine which server from the server farm should receive each client request. Your choice of algorithm is one way you can maximize the efficiency of your resources and obtain the type of experience you want. Here are five of the most common load balancing methods:
There are a variety of load balancing methods, which use different algorithms best suited for a particular situation. Here are some of the more prominent methods:
The Round Robin method relies on a rotation system to sort network and application traffic. An inbound request is delegated to the first available server, and then the server is bumped to the bottom of the line. This method is particularly useful when working with servers of equal value.
As its name states, the least connection method directs traffic to whichever server has the least amount of active connections. This is helpful during heavy traffic periods, as it helps maintain even distribution among all available servers.
Resource-Based (Adaptive) is a load balancing algorithm that requires an agent to be installed on the application server that reports on its current load to the load balancer. The installed agent monitors the application servers’ availability status and resources. The load balancer queries the output from the agent to aid in load balancing decisions.
In the least response time algorithm, the back-end server with the least number of active connections and the least average response time is selected. Using this algorithm IT ensures quick response time for end clients.
Fixed Weighting is a load-balancing algorithm where the administrator assigns a weight to each application server based on criteria of their choosing to demonstrate the application server’s traffic-handling capability. The application server with the highest weight will receive all of the traffic. If the application server with the highest weight fails, all traffic will be directed to the next highest weighted application server.
Weighted Response Time is a load-balancing algorithm where the response times of the application servers determine which application server receives the next request. The application server response time to a health check is used to calculate the application server weights. The application server that is responding the fastest receives the next request.
Source IP hash load balancing algorithm that combines source and destination IP addresses of the client and server to generate a unique hash key. The key is used to allocate the client to a particular server. As the key can be regenerated if the session is broken, the client request is directed to the same server it was using previously. This is useful if a client must connect to a session that is still active after a disconnection. This also ties back to session persistence where the clients session data would be obtained from the cookies saved in that server.
URL Hash is a load-balancing algorithm to distribute writes evenly across multiple sites and sends all reads to the site owning the object.
If you want a taste of what a load balancer is capable of then it doesn’t hurt to try out some of the leading companies who are bringing load balancing to the forefront. Among these is Scale Arc which serves as database load balancing software providing continuous availability at high-performance levels for mission-critical database systems deployed at scale.
The ScaleArc software appliance is a database load balancer. It enables database administrators to create highly available, scalable — and easy to manage, maintain, and migrate — database deployments. ScaleArc works with Microsoft SQL Server and MySQL as an on-premise solution, and within the cloud for corresponding PaaS, as well as DBaaS solutions, including Amazon RDS or AzureSQL.