1. Computer Systems seen as Auctions Milan Vojnović Microsoft Research Keynote talk ACM Sigmetrics 2010 New York, June 15, 2010

2. Two high-level points Auctions are in prevalent use in computer systems and services –Network protocols – bandwidth sharing –Cloud computing – compute instances –Online services – ad slots, buy-sell, crowdsourcing Auction theory is useful for performance evaluation and design of systems 2

3. Where the money goes Worldwide market of online services industry –$48 billion in 2011 –Projected $67 billion in 2013 (47% display ads, 53% search advertising) Cloud computing services 3

4. Roadmap Bandwidth auctions Online services Compute instances 1 4

5. Bandwidth auctions Every time you send an email you participate in an auction 5

6. Bandwidth auctions (cont’d) x1x1 x2x2 C/w C w P w 1 1 x1x1 x2x2 C C2C2 C2C2 C3C3 C1C1 x2x2 x1x1 P C2C2 C1C1 x1x1 C3C3 x2x2 P C C x2x2 x1x1 C x1x1 x2x2 6

7. Kelly’s game [Kelly ’97, …] xixi pipi C b1b1 bnbn bibi USER Allocation: Payment: USER 7

8. Kelly’s game (cont’d) Price taking users: Under price-taking users with concave, utility functions, efficiency is 100%. 8

9. Price anticipating users [Johari and Tsitsiklis ’04] Under price-anticipating users with concave, non-negative utility functions, and vector bids, the worst-case efficiency is 75%. USER: 9

10. Price anticipating users [Hajek and Yang ’04] Under price-anticipating users with concave, non- negative utility functions, and scalar bids, worst case efficiency is 0. Worst case: serial network of unit capacity links 10

11. But I want to maximize my revenue 11

12. Price discrimination Simple auctions with strategic users and providers? 12

13. Weighted proportional auctions [Nguyen & V. ’10] Allocation to user i: Payment by user i:b i 13

14. Roadmap Bandwidth auctions Online services Compute instances 2 14

15. Markets of compute instances Amazon WS Google Microsoft Azure Yahoo! … 15

16. Markets of compute instances (cont’d) 16

17. Markets of compute instances (cont’d) Ex. Amazon EC2 spot instances Cloud service provider 17

18. Roadmap Bandwidth auctions Online services Compute instances 3 18

19. Online services Selling of items –Ad slots (search, display, mobile, …) –Ebay More subtle examples –Crowdsourcing services (e-lance, online QnA, …) 19

20. Innocentive Odesk Witkey Topcoder Amazon Mechanical Turk … Online contests 20

21. Online contests 21

22. Incentives for contributions Monetary –Money Non-monetary –Reputation –Certificates and levels How do we design incentives so as to elicit desired contributions? 22

23. Ex. Yahoo! Answers’ points Points: Levels: 23

24. Witkey usage [DiPalantino, Karagiannis, V. ’10] 24

25. Seen as a system of competing auctions [Dipalantino & V. ’09] Each user selects (1) auction (2) bid R1R1 R2R2 RJRJ... RjRj contestsusers 1 2 u N... Properties at Bayes Nash equilibrium of the underlying game? 25

26. Equilibrium selection of auctions Partitioning over user skills m 0 1 2 3 4 5 1 v2v2 v3v3 v4v4 2 3 4 skill levels auction classes 26

27. Equilibrium participation in many auctions limit Prior-free Expected number of users in an auction of class j: 27

28. Model vs. measurements Mean number of submission versus reward Better conformance by conditioning on more experienced users any rate once a monthevery fourth dayevery second day 28

29. Other uses of auctions Weighted bipartite matching [Bertsekas ’79, Bayati et al ’08, …] Wireless spectrum auctions [FCC, …] Peer-to-peer file sharing [Levin et al ’08, Aperjis et al ’08] 29

30. Conclusion Auctions for designing and reasoning about computer systems and services 30

31. Extras: Elastic and super elastic [Key, Massoulie & V. ’05] Traditional elastic users –Ex. TCP Less traditional - super-elastic –Value allocation over long-run –Ex. peer-to-peer file sharing From time to phases 31

32. Extras: Submissions vs. reward Top 10 users – number of submissions 32 Reward Fraction of submitted solutions