Cluster-based load balancing for better network security

Gal Frishman, Yaniv Ben-Itzhak, Oded Margalit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In the big-data era, the amount of traffic is rapidly increasing. Therefore, scaling methods are commonly used. For instance, an appliance composed of several instances (scaled-out method), and a load-balancer that distributes incoming traffic 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-specific 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 loadbalancer 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 loadbalancers.

Original languageEnglish
Title of host publicationBig-DAMA 2017 - Proceedings of the 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2017
PublisherAssociation for Computing Machinery, Inc
Pages7-12
Number of pages6
ISBN (Electronic)9781450350549
DOIs
StatePublished - 7 Aug 2017
Externally publishedYes
Event2017 ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA 2017 - Los Angeles, United States
Duration: 21 Aug 2017 → …

Publication series

NameBig-DAMA 2017 - Proceedings of the 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2017

Conference

Conference2017 ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA 2017
Country/TerritoryUnited States
CityLos Angeles
Period21/08/17 → …

Keywords

  • Clustering
  • Flow distribution
  • Load-balancing
  • Machine learning
  • Misuse detection
  • Network security

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