1 / 19

EasyBid: Enabling Cellular Offloading via Small Players

EasyBid: Enabling Cellular Offloading via Small Players. Zhixue Lu 1 , Prasun Sinha 1 and R. Srikant 2. 1 The Ohio State University 2 Univ . of Illinois at Urbana-Champaign. Cellular Data Keeps Increasing. Mobile Data Increases more than 60% Annually

wren
Download Presentation

EasyBid: Enabling Cellular Offloading via Small Players

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EasyBid: Enabling Cellular Offloading via Small Players Zhixue Lu1, Prasun Sinha1 and R. Srikant2 1The Ohio State University 2Univ. of Illinois at Urbana-Champaign

  2. Cellular Data Keeps Increasing • Mobile Data Increases more than 60% Annually • Small Cells (Femtocells) Increase Spectrum Reuse

  3. Femtocells: the Concept • Small in-home Cellular Base Station • connects to the service provider’s network through owner’s broadband network Femtocell Internet Femtocell Gateway Broadband Router Core Network

  4. Femtocells: the Facts • To Deploy Cellular Base Stations • Site, Backbone and Power Supply • Costly to deploy • 7.9 Million Femtocells Deployed by 2013 • Almost all are residential and enterprise (small owners) Femtocells • Acquiring Access to these Femtocells is Important

  5. Proposed Incentive Mechanism: Auction • Why Auction? : Fair and Efficient • Two Types of Auctions • Forward Auction: buyers bid • Reverse Auction: sellers bid • Consider a Reverse Auction Model • Buyer: the wireless service provider (WSP) • Sellers: the femtocell owners • Reason: most owners have only one femtocell

  6. Background • Desired Properties of Auctions • Truthfulness: bidders cannot get higher utility by lying • Individual Rationality: utility of any bidder ≥0 • Common Auction Mechanisms • Secondary price auction • Reserve price based secondary auction

  7. Imprecise Valuation: an Ignored Problem • Existing Works Assume Precise Valuations • Valuations of Femtocell Owners Depend On: • Cost of extra broadband traffic, electricity usage • Degree of overload/delay tolerance • Wiliness to provide service • May vary over time • Hard to Precisely Estimate = ? + + No Delay!

  8. Assumptions • Sellers Can Estimate With Bounded Errors • : True Valuation of f, Hidden Value • : Perceived Valuation of f, Exposed Value • Distribution of is known • Truthful Auctions: Sellers Submit Perceived Valuations Truthfully

  9. Basic Form of Auctions in the Paper • Consider Reserve-Price based Secondary Auction • Secondary auction: truthful with precise valuations • Reserve price: eliminate errors (uncertainties) in payments • How It Works • Consider one seller a time • WSP sets a reserve price x • The Femtocell owner places its bid • Auction succeeds and pay x to the owner if the bid ≤ x • Utility of WSP is G-x, G: the savings of the WSP on each unit of data offloading

  10. Negative Utility of Femtocells • Femtocell Owners: Negative Utility when < Payment < • G=14,Uniform in [0,10] ,=2 • Reserve Price: x=$7 • : $8, : $6 • Negative utility: 7-8 = -1 • Individual Rationality Violated 6 10 4 2 0 8

  11. Address Negative Utility Issue (Naïve) • The WSP sets a reserve price $6, payment $8 • Seller f wins and receives $8 if its bid ≤ 6 • Expected Utility of WSP: 3.6 • =3.6 Reserve Price 4 Worst-case IR Payment 6 10 0 8 2 =2

  12. New Issue (Naïve): Imprecision Loss • For Femtocell Owners: • , No loss even if • , Loss if > 6 • , Loss if > 6 No Imprecision Loss Imprecision Loss No Imprecision Loss 4 6 10 0 8 2 Reserve Price Payment • Imprecision Loss(IL): Percentage of utility loss Due to Imprecision: 100%

  13. Problem Definition • M sellers, distribution of valuations known Problem: maximize Subject to: Sellers are comfortable to submit imprecise valuations No Imprecision Loss Imprecision Loss 1. The Worst-case Utility of any seller ≥0 2. Partial Truthfulness: percent do not lose any potential utility by submitting imprecise valuations 3. Imprecision Loss: The expected utility loss for each user (in red) is bounded () No Imprecision Loss 4 6 10 0 8 2 Reserve Price Payment

  14. Solution: Multiple Reserve Prices • Example: 2-reserve-price Approach: Segments: Si S1 S2 10 0 4 • if bid ∈ [0,4), approve and pay $8 • if bid ∈ [4,10], approve with probability 2/3 and pay $10 if it is approved • Truthful and IR with Precise Valuations Approval Ratios: Ri Payments: Pi

  15. Multiple Reserve Prices In Imprecise Valuation Auction • Two Reserve Prices No Imprecision Loss Imprecision Loss No Imprecision Loss S1 S2 0 4 10 6

  16. Algorithm Sketch • Input • (Saving of WSP) • (Estimation Error) • Distribution of • (Constraints) • Output • $N$ Reserve Prices (Si,Ri, Pi, ) • Dynamic Programming based Algorithm: Pseudo-polynomial Time Complexity

  17. Example Seller #2 Seller #4 S1 S2 $6 A $3 C D B $1 0 4 10 E 6 $8 Seller #1 Seller #3

  18. Simulation Result • Precise Valuation • Near Optimal • Imprecise Valuation • Increasing Decreases • DDecreases

  19. Summary • EasyBid: A Reverse Auction Mechanism for Acquiring Access to Femtocells • Introduce the notion of Perceived Valuation, Partial Truthfulness, and Imprecision Loss to characterize the quality of auctions with imprecise valuations. • Present heuristic algorithms to maximize the WSP’s utility while satisfying given constraints on partial truthfulness and imprecision loss.

More Related