iDEAL : Incentivized Dynamic Cellular Offloading via Auctions

1. iDEAL: Incentivized Dynamic Cellular Offloading via Auctions Wei Dong1 Joint work with Swati Rallapalli1, Rittwik Jana2, Lili Qiu1, K. K. Ramakrishnan2, Leonid V. Razoumov2, Yin Zhang1, Tae Won Cho2 1The University of Texas at Austin 2 AT&T Labs – Research INFOCOM 2013

2. Motivation • Cellular network overloaded • Traffic is highly dynamic • Large peak-to-average traffic ratio • Over 5 times in this example • => • Too costly to provision based on the peak demand Time (s) Cellular demand variation

3. Alternative? • ISPs augment cellular networks on their own • Wi-Fi • Femtocell • Insufficient as a long-term solution • High deployment cost • High management cost • Interferes with existing infrastructure

4. Our approach • Cellular provider purchases bandwidth on demand from 3rd party resources • Wi-Fi, femtocell, or other cellular resources • Incentivize cellular offloading via auctions • Effective price discovery • Avoids long-term contracts • Cut cost by leveraging the competition

5. Unique challenges • Diverse spatial coverage • Traffic uncertainty • Non-truthful bidding and collusion

6. Cellular offloading as a reverse auction Auction conducted periodically Cellular provider (A): buyer Hotspots: sellers Hotspots submit bids Cellular provider (A) serves as auctioneer A sector is divided into regions based on location of hotspots Objective: satisfy A’s traffic while minimizing the total cost Cost = cellular cost + hotspots cost A R1 R2 R3

7. Naïve solution Limited competition R1 R2 Auction 2 Demand 2 Auction 1 Demand 1 Cellular resource Cellular resource as a virtual bidder  Inter-region competition

8. iDEAL overview • Two phases of iDEAL • Allocation: determine how to allocate cellular resources and 3rd party resources to minimize cost • Pricing: determine payment to 3rd party resource owners

9. Global static allocation • Input: di, ei, λj, pj, F(z) • Output: xj, ci, z • Minimize: • Subject to: • [C1] • [C2] • [C3] • [C4] Total cost: Wi-Fi + cellular Satisfy the demand with both Wi-Fi and cellular Translate cellular resource usage into spectrum Never buy more than offered

10. Global dynamic allocation • Traffic in different regions may peak at different times • Have multiple possible demand vectors • Optimize for the worst case • [C1-dynamic] • [C2-dynamic] • Benefits • Efficiently leverage cellular resource on demand • Avoid provisioning for the peak in each region

11. iDEAL pricing • VCG principle • Pay the winners the opportunity cost • Payment to winner w = the extra amount that other bidders could sell if w is not present • iDEAL pricing • Apply VCG over the whole sector as one auction • Global opportunity cost • Captures inter- and intra-region competition

12. iDEAL pricing: Example r: amount of resource v: valuation of a single unit d: demand Allocation if 1 is not there r:1 v:$1.5 Optimal allocation Value sold by others: $3.5 Global opportunity cost: $2 1 Consider hotspot 1 Value sold by others: $1.5 r:1 v:$1 2 3 The “local opportunity cost” is $9 r:1 v:$2 r:1 v:$9 Region 2 (d:1) Region 1 (d: 1) Global opportunity cost captures inter-region competition and lowers cost Payment is higher than bid

13. Economic properties • Theorem 1: Truth-telling is optimal. • Theorem 2: iDEAL is efficient (i.e., winners are the bidders with lowest valuation). • Theorem 3: iDEAL is individually rational (i.e., bidders of the auction will get non-negative utility).

14. Understand collusions • Collusion strategies • Single seller collusion • Multi seller collusion • In both cases, they useSupply Reduction • Drop losing bids • Reduce the capacity offered in winning bids • Supply reduction: increases the opportunity cost  drives up price

15. Mitigating collusions • Dynamic demands make collusion hard • Inaccurate traffic prediction  supply reduction may lead to missed winning opportunities • Bidding as a group • Hotspots owned by same party  bid as group  considered one bidder • Removes competition within a group • no incentive for supply reduction • Inter-group competition retained • Multi seller collusion is unstable • Seller has incentive to leave the bidding ring

16. Evaluation methodology Sector reports # of users Event driven trace player 3G HTTP sessions Detailed aggregated traffic demand Sector location Regions Clustering Hotspot location Pricing plan of major ISPs Bids Auctions

17. Comparison of truthful auctions 40 hotspots 130 hotspots Auction based approaches much better than fixed pricing given enough competition iDEAL consistently beats other auction based approaches

18. Comparison of truthful auctions (cont.) 40 hotspots 130 hotspots Global allocation efficiently allocates cellular resource to different regions. Dynamic global allocation avoids provisioning for peak demand in each region.

19. Comparison of non-truthful auctions Value consumption Cost Non-truthful auctions invites gaming behaviors Gaming causes fluctuation and can increase cost VCG in iDEALis stable, efficient and low-cost

20. Collusion under dynamic demands • Used two different sizes of bidding ring: 20 and 50 50: 28% chance of higher utility 20: 5% chance Significantly weakens the incentive to collude

21. Collusion: Bidding as a group 40 hotspots 130 hotspots Group bidding removes competition between a seller’sown hotspots and maintains the competition between different sellers, thus reducing the cost

22. Conclusion • Design incentive framework for cellular offload • Explicitly account for diverse spatial coverage of different resources • Cope with dynamic traffic • Promote truthfulness • Provably efficient • Guard against collusions • Trace-driven simulations show iDEAL is efficient, low-cost and robust against collusion

23. Q&A Thank you

24. Backup slides

25. Practical Considerations • Supporting offloading to femtocells and dynamic roaming • The same framework applies to purchasing femtocells and other cellular resources • Handle partially overlapping spatial coverage • Revise the constraint [C1] to split the resources from the same provider into different regions

26. Related Work • Measurement • Balasubramanian et al. report Wi-Fi is available for 11% time and 3G is available for 87% time but they are negatively correlated • Lee et al. find Wi-Fi offload 65% traffic without delay and 83% with over 1-hour delay • Auction based offloading • Zhou et al. uses auction to incentivize mobile users to wait until they reach Wi-Fi • Chen et al. uses auction to incentivize femtocell owners to share resources • Ignore three unique challenges iDEAL addresses • Similar to local allocation in spirit

27. Design Goals • Account for different spatial coverage of resources • Achieve high efficiency • Promote truthful bidding • Low cost • Guard against collusion

28. Mitigating Collusions (Cont.) • Stability of a multi-seller collusion • Without utility sharing, a seller has no incentive to conduct supply reduction • Follows from the truthfulness of VCG and that in our system sellers submit sealed, separated bids • Utility sharing is hard to achieve in our system • Hard to attribute utility change to collusion • Demand and Wi-Fi availability is dynamic • Hard to validate other bidders’ behavior • Sellers submit sealed separately bids • Can make it even harder via system design • E.g. use delayed payment to further obfuscate the utility

29. Supporting Femtocell Offload Benefit of femtocells is large when there are fewer Wi-Fi hotspots 16 femtocells

30. Supporting Dynamic Roaming When Wi-Fi is insufficient, dynamic roaming can significantly cut down cost even with a small amount of capacity 40 hotspots

31. Implementation • Dynamic offloading involves three steps • Identify a network to offload • Solved by iDEAL • Automatic authentication • Solved by Hotspot 2.0 • The roaming partners are updated dynamically according to the offload decision from iDEAL. • Seamless offload to maintain the existing sessions • Addressed by Dual Stack Mobile IP (DSMIP), DSMIPv6, …

32. System Architecture