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.




Research Areas

  • Load Balancer
  • Machine Learning
  • Middle boxes / network appliances
  • NIDS




Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks