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![MDO
Introduction to MDO
Why MDO?
Huge increase in Wi-Fi enabled devices [Cisco,2016]
Wi-Fi devices: 1.7 Billion
Cellular devices: 548 millions
Can we use these Wi-Fi access points?](https://image.slidesharecdn.com/mobiledataoffloading-180701201742/75/Mobile-data-offloading-6-2048.jpg)
![MDO
Offloading System Model
Mobile Data Offloading Scenario
Typical offloading system has
A set of MNOs, κ
∆
= {1, 2, ..., K}
A set of BSs, M
∆
= {1, 2, . . . , M}
A set of APs, I
∆
= {1, 2 . . . , I}
Figure: Offloading scenario [Iosifidis, 2015]](https://image.slidesharecdn.com/mobiledataoffloading-180701201742/75/Mobile-data-offloading-10-2048.jpg)
Uploaded byAkshay Salunkhe
Mobile data offloading
This document provides an overview of mobile data offloading techniques for next generation cellular networks. It discusses the expected growth in mobile data traffic and need for offloading to WiFi networks. It presents a model for the offloading system involving mobile network operators, base stations and access points. It formulates the offloading problem as an optimization to maximize social welfare. An iterated double auction mechanism is proposed to solve the optimization in a distributed manner while achieving the desired economic properties. Results show the mechanism enables the requests and admissions to converge over iterations, minimizing the demand gap.
Mobile data offloading
- 1. MDO Overview of MobileData Offloading Techniques in the Next Generation Cellular Networks Akshay A. Salunkhe Roll No.: 183021002 Department of Electrical Engineering Indian Institute of Technology Dharwad
- 2. MDO Overview 1 Introduction toMDO 2 Offloading System Model Terms Need of broker 3 Social Welfare Maximization Lagrangian KKT 4 Iterated Double Auction Mechanism 5 Results Evolution of social welfare Demand Gap 6 Conclusions
- 3. MDO Introduction to MDO ExpectedData Traffic Growth Figure: Expected Traffic Growth (source: CISCO VNI Mobile 2016) Mobile data traffic will grow at a Growth Rate of 53 % from 2015 to 2020 The amount of traffic offloaded from smartphones 64% by 2021, compared to 58% in 2016 Most of traffic from social media.
- 4. MDO Introduction to MDO Waysto Support This Huge Demand Acquiring new spectrum bands Developing high frequency technology Upgrading access technology Building more pico/micro cell sites Drawbacks... Need heavy investments
- 5. MDO Introduction to MDO OtherOptions Throttle speed Time/location/congestion dependent pricing Mobile Data Offloading (MDO)
- 6. MDO Introduction to MDO WhyMDO? Huge increase in Wi-Fi enabled devices [Cisco,2016] Wi-Fi devices: 1.7 Billion Cellular devices: 548 millions Can we use these Wi-Fi access points?
- 7. MDO Introduction to MDO WhyMDO? Beneficial to all For users, Reduces monthly bill Increases battery life of mobile devices For MNOs, Reduces installation costs for new BSs For AP owners, Compensation for the service
- 8. MDO Introduction to MDO Objectives Studyoffloading techniques Formulate MDO as an optimization problem Use Iterated Double Auction mechanism to solve the optimization problem Compare results with optimal social welfare
- 9. MDO Introduction to MDO DataOffloading Through: Small Cell Networks (SCNs) Wi-Fi networks Opportunistic mobile networks Heterogeneous networks
- 10. MDO Offloading System Model MobileData Offloading Scenario Typical offloading system has A set of MNOs, κ ∆ = {1, 2, ..., K} A set of BSs, M ∆ = {1, 2, . . . , M} A set of APs, I ∆ = {1, 2 . . . , I} Figure: Offloading scenario [Iosifidis, 2015]
- 11. MDO Offloading System Model Terms Terms... Offloadrequest vector for BS m to all APs is xm ∆ = (xmi : ∀i ∈ I) e.g. for 3 APs, first Base Station(m=1), x1 = 10 15 5 Stacking these xi , gives offload request matrix xk xk = (xm : ∀m ∈ MK ) e.g. for first MNO (k=1) with 3 BSs, 3 APs, x1 = 10 2 5 15 3 10 5 5 10
- 12. MDO Offloading System Model Terms TermsContd... Utility of MNO k when its BS m offloads traffic xm to APs is given by Jm (xm) , m ∈ Mk Total offloading benefit for operator k is Jk (xk) = Mk m=1 Jm (xm) . (1) Assume data service cost to be strictly convex so utility functions are strictly concave.
- 13. MDO Offloading System Model Terms TermsContd... Admitted traffic vector for AP i is yi ∆ = (yim : ∀m ∈ M) . yim depends on respective request xmi Cost incurred by AP i for serving MNOs is Vi (yi) Utility and Cost functions as a function of offloaded data are Figure: Utility and Cost functions
- 14. MDO Offloading System Model Needof broker Need of Broker MNO would like to offload its traffic at minimum cost AP would like to get maximum returns for the offloading service they provide Objectives of MNOs and APs conflict,hence a broker is needed.
- 15. MDO Social Welfare Maximization OptimizationProblem Broker obtains optimal x and y by solving Social Welfare Maximization (SWM) problem maxx,y K k=1 Jk (xk) − I i=1 Vi (yi) , (2) subject to, M m=1 yim C + j∈I{i} M m=1 γij yjm Cj ≤ 1, ∀i ∈ I, (3) yim ≥ xmi , ∀m ∈ M, ∀i ∈ I, (4) xmi ≥ 0, yim ≥ 0, ∀m ∈ M, ∀i ∈ I. (5)
- 16. MDO Social Welfare Maximization Lagrangian Lagrangianfor SWM. . . Lagrangian of the problem can be written as L (λ, µ, x, y) = K k=1 Mk m=1 Jm(xm) − I i=1 Vi (yi) − I i=1 λi . I j=1 M m=1 γij yjm Cj − 1 − M m=1 I i=1 µmi . (xmi − yim) , (6) where, λ ∆ = (λi ≥ 0 : ∀i ∈ I) is the vector of lagrange multipliers and µ ∆ = (µmi ≥ 0 : ∀m ∈ M, ∀i ∈ I) is a matrix of lagrange multipliers.
- 17. MDO Social Welfare Maximization KKT KKTconditions for SWM. . . KKT conditions that yield optimal dual variables λo, µo and optimal primal variables xo, yo for the SWM problem are given by following set of equations ∀k ∈ K, ∀m ∈ M, ∀i ∈ I (A1) : ∂Jm (xm o) ∂xmi = µmi o , (A2) : ∂Vi (yi o) ∂yim = µmi o − I j=1 γji λi o Ci , (7) (A3) : λi o . M m=1 I j=1 γji λi o Ci − 1 = 0, (A4) : yim o ≥ xmi o , (8) (A5) : µmi o . (xmi o − yim o ) = 0, (A6) : xmi o , yim o , λi o , µmi o ≥ 0. (9)
- 18. MDO Social Welfare Maximization KKT DesiredEconomic Properties Broker is unaware of the utility and cost functions. Thus broker designs a mechanism with following economic properties, It should maximize the social welfare. It should be capable of retrieving private information of bidders. Payments from MNOs to broker should be equal or larger than total payment of broker to APs. MNOs and APs should not get negative payoffs by participating since they can choose not to participate and get zero payoffs.
- 19. MDO Iterated Double AuctionMechanism Iterative Double Auction mechanism Various steps involved in Iterative Double Auction mechanism are The broker initializes variables x, y, λ and µ. Each MNO computes optimal bids. Each AP computes optimal bids. AP and MNO submit bids to broker. Broker computes new primal variables x, y. Broker updates dual variables λ, µ using gradient descent method. Broker terminates iterations if the allocations converge to stable values. Broker finalizes payment by each MNO and compensation to each AP.
- 20. MDO Results Evolution of socialwelfare Performance of IDA A small market with K=2 MNOs and I=5 APs is considered. Social Welfare achieved by the algorithm is plotted. It slowly converges to optimal Social Welfare. Figure: Evolution of social welfare produced by the IDA, for a market with 5 base stations, 5 access points. Algorithm always converges for varying values of MNOs and APs.
- 21. MDO Results Demand Gap Gap betweenrequested and admitted data In Figure below, convergence of x and y is shown. Gap between x and y is plotted. Figure: Demand Gap
- 22. MDO Conclusions Conclusions and FutureWork Convergence of IDA was tested In this study, bidders are assumed to be price takers In Future, work can be extended to study market mechanisms considering price anticipating behaviors of MNOs and APs.
- 23. MDO Conclusions References George Iosifidis, LinGao A Double-Auction Mechanism for Mobile Data-Offloading Markets IEEE/ACM Transactions on Networking vol. 23, no. 5, pp. 1634-1647, Oct. 2015. Lee K., Chong S. Mobile Data Offloading: How Much Can WiFi Deliver? Proceedings of the 6th International Conference, Co-NEXT . Cisco (2016) Global Mobile Data Traffic Forecast Update, 2015–2020 Cisco Visual Networking Index Feb. 2016. Yetim O.B., Martonosi M. Dynamic adaptive techniques for learning application delay tolerance for mobile data offloading Proceedings of IEEE INFOCOM, Apr./May 2015, pp. 1885-1893.
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