Naïve Bayes Classifier-Assisted Least Loaded Routing for Circuit-Switched Networks

Abstract

Circuit switching is a de facto switching technology widely employed in today's networks where the conventional approaches to routing have remained unchanged for many years. This paper develops a new and very different methodology, by incorporating a supervised naïve Bayes (NB) classifier, to assist least loaded (LL) routing and to further improve its performance that has remained the best among all the routing approach for the past several decades. Specifically, by iteratively learning the information of historical network snapshots, the NB classifier predicts potential future circuit blocking probability between each node pair if a service connection is established via a certain route between the node pair. The snapshots are taken for each service request arriving at an operating network that keeps on accepting and releasing dynamic service connections and records the number of busy capacity units on each link at each snapshot instance. The candidate route for serving a new request is selected based on both link loads and the potential future blocking probability in the entire network in case that this route is indeed used. The performance of the proposed approach is studied via simulations and compared with the conventional LL algorithm. The results indicate that the supervised NB classifier-assisted LL routing algorithm effectively reduces the blocking probability of service connections and outperforms the conventional LL routing algorithm. To speed up the learning process (which is based on a large number of network snapshots), we also develop a framework to incorporate the proposed approach in a parallel learning system. A network control system supporting online NB classifier-assisted LL routing algorithm is also described.

Keywords

• Machine learning
• naïve Bayes classifier
• least loaded routing
• blocking probability
• circuit switched network