Optimizing callout in unified ad markets

Aman Gupta, S. Muthukrishnan, Smita Wadhwa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the past, online ad networks owned their supply of impressions for ads with publishers and matched it to their own demand (advertisers). However, in the past few years, with programmatic ad selling becoming common place, these ad networks are now appealing to demand from multitude of demand side platforms (DSPs). The general approach is to partition the demand into pools (based on type of ads or the demand party). An inevitable challenge is the impedance mismatch between the marketplace and the demand pools. Thus, a central problem is to figure out how to handle each supply request and selectively send requests to demand pool so as to optimize the performance of entire supply. This is the Callout Problem. We present a large scale data analysis and control system that (a) continually learns changing traffic patterns, capacities, bids, revenue etc, and (b) picks the best slice of traffic to send to each demand pool, subject to their capacity constraints. The optimization is based on a greedy solution to the underlying knapsack problem which easily adapts as capacities change over time. We have implemented and deployed this solution for the callout problem in one of the largest mobile ad marketplaces(InMobi) and has been operational for several months. In this paper, we will describe the scale of the problem, our solution and our observations from operational experience with it. We believe a well-engineered solution to the Callout problem is essential for many ad networks in the online ad world.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1315-1321
Number of pages7
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

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