This blog discusses how load balancing in the cloud differs from a traditional network traffic distribution and explores various services available from top cloud providers – AWS, Google Cloud, and Microsoft Azure.
Load balancing is a process of distributing network traffic across multiple instances of a workload. The IT teams practice load balancing to make sure that each instance performs at its peak efficiency. While maintaining peak efficiency, it is ensured that instances are not overburdened or fail due to excess network traffic.
How load balancing in cloud differs from a traditional network traffic distribution?
Generally, a load balancer finds its place in a local data center as a dedicated physical network appliance. Load balancing is often performed by an application installed on a server and offered as a network service. Public cloud providers use the service model and provide software-based load balancers as an exclusive feature.
The load balancer acts as a network front-end once it is implemented. It often uses a single IP address to receive all network traffic routed for the target workload. The load balancer can distribute the network traffic evenly to each available workload instance, or its control and send the specific percentages of traffic to each instance.
In a general sense, with a load balancer, the target workloads can be in different physical locations. Cloud load balancing also enables users to distribute network traffic across multiple instances within the same region or across various regions or availability zones (AZs).
Load Balancing Depends on Layers
Have you heard about the Open Systems Interconnection Model (often knows as OSI model)? Load balancing is defined by the layer based on the OSI model. Every layer resembles a specific traffic type. Cloud load balancing is performed at Layer 4 (transport or connection layer) or Layer 7 (application layer).
To manage load at Layer 4, the top three cloud providers have different services. AWS’ Network Load Balancer service operates to route data from transport layer protocols, including Transmission Control Protocol (TCP), along with User Datagram Protocol (UDP), and Transport Layer Security (TLS). However, Google Cloud refers to this as TCP/UDP Load Balancing, and Microsoft calls it service at this layer as Azure Load Balancer.
When traffic is handled at a lower layer of the network stack, it appears to provide the best performance. In other words, Layer 4 load balancing is much easier and more efficient. With vast resources at disposal, cloud load balancing can handle millions of network requests per second and ensure low latencies.
When it comes to the top of the network stack, the load balancing at Layer 7 handles complex traffic side, such as HTTP and HTTPS requests. The major cloud providers have their feature or service for this – AWS Application Load Balancer, Azure Application Gateway, and Google Cloud HTTP(S) Load Balancing.
Load balancing could be complex at higher layers but comes with more advanced options such as IT teams that can route traffic based on content or requests. This type of cloud balancing complements the modern applications instances and architectures, including microservices and container-based workloads.
Cloud Load-balancing is way more than traditional load balancing
There are minute details that users should focus on when it comes to choosing a cloud load balancer. Cloud providers can also differentiate load-balancing services based on the scope and framework. For example, Google Cloud offers global load balancing services when workloads are distributed across multiple regions. Similarly, regional load balancing services are provided when all workloads are in the same region. Not only this, but GCP also offers external load balancers when traffic is coming from the internet into the workloads and internal load balancers when traffic is routed within GCP.
It is essential to look beyond the basic cloud load-balancing features that top cloud providers offer. These features may include support for single front-end IP address, support for the automatic workload scaling, and integration with various other cloud services, such as monitoring and alerting.