Ad Exchange Optimization Algorithms on Advertising Networks

Abstract

Online advertising has seen great growth over the past few years. Advertisers have gotten better results with campaigns targeted at more specific audiences. Ad networks with few visits are unable to create such campaigns and hence are moving forward towards a new model, consisting of a huge global ad exchange market. In this market millions of advertisers compete for the ad space so that their ad will be shown to users upon visiting a page. In selecting the best candidate from all possibilities algorithms able to process advertiser's requirements in tenths of seconds are needed. To face this problem we have developed algorithms using techniques such as threads, AVL trees with hash, multiple node trees or Hadoop technology. Throughout this article we will show the results gained from each algorithm, a comparative performance analysis and some conclusions. We have also proposed some future lines of work.