Delay-cost Optimal Coupon Delivery in Mobile Opportunistic Networks

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1 Delay-cost Optimal Coupon Delivery in Mobile Opportunistic Networks Srinivasan Venkatramanan Joint work with Prof. Anurag Kumar Department of ECE, IISc. January 9, 214

2 Mobile Opportunistic Networks (MON) Proliferation of smart mobile devices Smartphones, tablets, wearable computing devices, etc. Mobile content delivery - a challenge Direct delivery - not scalable Alternative: p2p delivery (via Bluetooth, WiFi-Direct, etc.) A single item of content may be of interest to several co-located users e.g., slides of a conference keynote/ course lecture The item can be forwarded between devices when their p2p radio interfaces make contact Akin to epidemic spread A multi-hop opportunistic mobile network Provides an approach to delay tolerant networking

3 Mobile Coupon Delivery Ecosystem Scenario: Pre-release promotions of a product (movie, book, etc.) Content: discount coupon for pre-ordering the product Mobile users can maintain a wishlist of products of interest share wishlists with peers receive coupons from peers or central server wishlists + peer recommendation + coupon delivery

4 A Possible Application Framework node A node B node C node D want reco have want reco have want reco have want reco have Wishlist Exchange Wishlist Exchange Popularity Spread ÛºÔº Γ Ø Û ÒØ ½ Ö Ó ½ No state change Popularity Spread Update Wishlist Û ÒØµ ½ ÓÔÝ ÓÒØ ÒØ Ð ÓÔÝ ÓÒØ ÒØ ÛºÔº σ Digital Coupon Content Spread ÓÔÝ ÓÒØ ÒØ Ò Û ÒØµ ½ Digital Coupon Content Spread

5 Content Popularity and Dissemination: SIR-SI model Population size: N (xed) Pairwise meetings at points of independent Poisson processes The coupon needs to reach certain destination nodes Some destination nodes are given the coupon initially The set of destination nodes grows, as more nodes express their interest Do-not-want nodes could help in forwarding (relays) Objective: Quickly spread content to a large fraction of destinations nodes while minimizing the residual number of relays that have the content

6 SIR-SI States and Evolution have don t have don t want (relays) want (infectious destinations) want (non infectious destinations) Y X d X b S Y D X d B X b λ N : Poisson meeting rate for each pair of nodes; λ N = λ N β N : Recovery rate of infectious destinations; β N = β Γ: Inuence probability σ: Copying probability to a relay (control) Influence spread (SIR Model) Content spread (SI Model) Influence & Content spread

7 Fluid Limits - Kurtz Theorem 1 λ λ µ(1 λ) 1 λ(1 µ) λ(1 µ) λ(1 µ) λ(1 µ) λ(1 µ) 2 n 1 n n+1 µ(1 λ) µ(1 λ) µ(1 λ) 1 µ(1 λ) λ(1 µ) Queue length process, X (k), k, X (N) (t) := 1 N X ( Nt ) ODE: ẋ(t) = (λ µ)i {x(t)>} with x() = X (N) (), for each N (1/n)X( nt ), x(t) x(t) n=1 n=1 n=1 (1/n)X( nt ), x(t) x(t) n=1 n=1 n= t t

8 SIR-SI CTMC Markov Chain: Transitions at Meeting Epochs Let k =, 1, 2, index the meeting/recovery epochs at times t k System state: Z(k) = (B(k), D(k), X b (k), X d (k), Y (k)) Epoch type Rate State update δ k D X d recovers β N(D(k) X d (k)) (1,-1,,,) X d recovers β N X d (k) (1,-1,1,-1,) B X b meets X + Y λ N(B(k) X b (k))(x (k) + Y (k)) (,,1,,) D X d meets Y λ N(D(k) X d (k))y (k) (,,,1,) + (,1,,1,-1) w.p. Γ D X d meets X λ N(D(k) X d (k))x (k) (,,,1,) X d meets Y λ N X d (k)y (k) (,1,,1,-1) w.p Γ S Y meets X b + Y λ N(X b (k) + Y (k))(s(k) Y (k)) (,,,,1) w.p. σ S Y meets D X d λ N(D(k) X d (k))(s(k) Y (k)) (,1,,,) w.p. Γ S Y meets X d λ N(X d (k))(s(k) Y (k)) (,1,,1,) w.p. Γ (,,,,1) w.p(1 Γ)σ ḃ = βd ḋ = βd + λγds ẋ b = βx d + λ(b x b )(x + y) ẋ d = λ(d x d )(x + y) + λγdy + Γλx d (s y) βx d ẏ = Γλdy + λσ(s y)(x b + y + (1 Γ)x d ) where s(t) = 1 b(t) d(t)

9 Convergence of the CTMC to O.D.E. Limit Population fraction Population fraction a(t) x(t) y(t) N=1 N=1 N=5 o.d.e. Γ =.9, β =.3, λ =.3 d() =.2, x d () =.1 σ = Time (t) λ=.3, β =.3, Γ =.9 a(t) d() =.2, x d () =.1.6 x(t) σ =.3 y(t) N=1 N=1 N=5 o.d.e Time (t)

10 SIR-SI Model: Dynamic Control of Copying Dynamic control σ : Z(k) [, 1] CTMDP for each N: Obtaining optimal control is dicult Replace probabilistic control σ in the ODE by σ(t) (controlled ODE) Optimal (deterministic, open-loop) control for the controlled ODE Can be shown to be asymptotically optimal for the nite size problem Population fraction a(t) x(t) y(t) N=1 N=1 N=5 o.d.e. λ =.3, β =.3, Γ =.9 d()=.2, x d () =.1 Time threshold τ = Time (t)

11 Optimal Control Target time:t σ = inf{t : x σ (t) αa( )} a( ) = b( ) + d( ): terminal fraction of destinations x σ (t): fraction of destinations that have the coupon at time t Cost function: C σ = ψy σ (T σ ) + T σ = ψy σ (T σ ) + T σ 1dt y σ (T σ ) : fraction of relays that have the coupon at time T σ Theorem For the above o.d.e. system, for the cost function displayed earlier, there exists an optimal control of the form, σ τ (t) = { 1, < t < τ, t τ Distributed implementation: Time stamping of the coupon

12 Optimality of a Time Threshold Control: Sketch of Proof Dene Kamke dominance: extension of Kamke condition for o.d.e.s Here Kamke dominance holds, hence t, σ (1) (t) σ (2) (t), and (x (1) (1) (), x (), b d y (1) ()) (x (2) (2) (), x (), b d y (2) ()), implies that, t, (x (1) (1) (t), x b d (t), y (1) (t)) (x (2) b (2) (t), x (t), d y (2) (t)) Consider σ(t) (any action function) and σ τ (t) (a time threshold action function) such that y σ (T σ ) = y στ (T στ ) = ρ We can show that Tσ τ Tσ and hence the time threshold action function is optimal in {σ( ) : yσ(tσ) = ρ} Dene ρ max = max{y στ (T στ ) : τ } Consider an action function σ(t) with yσ(tσ) > ρ max The threshold policy with τ := sup{t : σ(t) > } has lower cost

13 Optimal Control for the Running Example C σ T σ y σ (T σ ) α =.95, ψ =6 λ=.3, β=.3, Γ=.9 d()=.2, x d ()= τ * Time threshold (τ) α =.95, C σ = 6y σ (T σ ) + T σ The optimal control is to copy until τ = 1.1 and then stop copying

14 Conclusion and Future Work A possible application framework for coupon delivery Delay-cost optimal forwarding Joint evolution of content delivery and popularity Modeled as CTMC and obtained the uid limits Existence of Time-threshold control which is delay-cost optimal Performance of optimal uid policy for the nite N case Possible extensions: Multiple items of content; communities of interest Large content: divided into several chunks Service pricing, and incentive mechanisms for relays

15 References Chandramani Singh, Anurag Kumar, Rajesh Sundaresan and Eitan Altman, Optimal Forwarding in Delay Tolerant Networks with Multiple Destinations, IEEE/ACM Transactions on Networking (TON) 213. SV and Anurag Kumar, Coevolution of Content Popularity and Delivery in Mobile P2P Networks, IEEE Infocom'12 (mini-conference) Shakkottai, S. and Johari, R., Demand-aware content distribution on the internet, IEEE/ACM Transactions on Networking (TON) 21 Thomas G. Kurtz, Solutions of Ordinary Dierential Equations as Limits of Pure Jump Markov Processes, J. Appl. Prob. 7, (197) N. Gast, B. Gaujal, and J. Boudec, Mean eld for Markov decision processes: from discrete to continuous optimization, Arxiv preprint arxiv: , 21. H. Smith, Monotone dynamical systems: An introduction to the theory of competitive and cooperative systems. American Mathematical Soc., 1995.

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