Where should distributed load balancing occur in a horizontally scalable architecture?
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A. B. C. D.B.
In a horizontally scalable architecture, load balancing is used to distribute incoming network traffic across multiple servers or instances to ensure that no single server or instance is overloaded with requests. This improves the availability, performance, and scalability of the application.
There are different types of load balancing, including firewall-side/policy load balancing, network-side/central load balancing, service-side/remote load balancing, and client-side/local load balancing.
Firewall-side/policy load balancing refers to the use of a firewall or policy engine to distribute incoming network traffic across multiple servers. This approach is typically used to manage traffic at the network perimeter and to ensure that incoming requests are directed to the appropriate server based on predefined policies.
Network-side/central load balancing refers to the use of a dedicated load balancer or load balancing service to distribute incoming network traffic across multiple servers. This approach is typically used to manage traffic within a data center or across multiple data centers.
Service-side/remote load balancing refers to the use of a load balancing service that is integrated into the application itself. This approach is typically used for microservices-based architectures, where each microservice is responsible for its own load balancing.
Client-side/local load balancing refers to the use of client-side software or libraries to distribute incoming network traffic across multiple servers. This approach is typically used for mobile or web applications, where client devices can connect to multiple servers simultaneously.
In general, the choice of load balancing approach depends on the specific requirements of the application and the architecture. However, in a horizontally scalable architecture, load balancing typically occurs at the network-side/central or service-side/remote level, as these approaches provide the greatest scalability and flexibility.