Cluster-based load balancer for scaled-out machine-learning-based appliances.
In the big-data era, the amount of traffi c is rapidly increasing. There-
fore, scaling methods are commonly used. For instance, an appli-
ance composed of several instances (scaled-out method), and a
load-balancer that distributes incoming traffi c among them. While
the most common way of load balancing is based on round robin,
some approaches optimize the load across instances according to
the appliance-specifi c functionality. For instance, load-balancing
for scaled-out proxy-server that increases the cache hit ratio.
In this paper, we present a novel load-balancing approach for
machine-learning based security appliances. Our proposed load-
balancer uses clustering method while keeping balanced load across
all of the network security appliance’s instances. We demonstrate
that our approach is scalable and improves the machine-learning
performance of the instances, as compared to traditional load-balancers.