Online Stochastic Packing Applied to Display Ad Allocation

  • Jon Feldman
  • Monika Henzinger
  • Nitish Korula
  • Vahab S. Mirrokni
  • Cliff Stein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6346)

Abstract

Inspired by online ad allocation, we study online stochastic packing integer programs from theoretical and practical standpoints. We first present a near-optimal online algorithm for a general class of packing integer programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple dual training-based algorithm achieves a (1 − o(1))-approximation guarantee in the random order stochastic model. This is a significant improvement over logarithmic or constant-factor approximations for the adversarial variants of the same problems (e.g. factor \(1-{1\over e}\) for online ad allocation, and log(m) for online routing). We then focus on the online display ad allocation problem and study the efficiency and fairness of various training-based and online allocation algorithms on data sets collected from real-life display ad allocation system. Our experimental evaluation confirms the effectiveness of training-based algorithms on real data sets, and also indicates an intrinsic trade-off between fairness and efficiency.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jon Feldman
    • 1
  • Monika Henzinger
    • 2
  • Nitish Korula
    • 3
  • Vahab S. Mirrokni
    • 1
  • Cliff Stein
    • 4
  1. 1.Google ResearchNew YorkU.S.A.
  2. 2.University of ViennaAustria
  3. 3.University of Illinois at Urbana-Champaign 
  4. 4.Google Research and Columbia UniversityNew York

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