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Two-Market Inter-domain Bandwidth Contracting Anusha Uppaluri, University of Nevada, Reno Praveen Kumar, Rensselaer Polytechnic.

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Presentation on theme: "Two-Market Inter-domain Bandwidth Contracting Anusha Uppaluri, University of Nevada, Reno Praveen Kumar, Rensselaer Polytechnic."— Presentation transcript:

1 Two-Market Inter-domain Bandwidth Contracting Anusha Uppaluri, University of Nevada, Reno (anusha.uppaluri@gmail.com) Praveen Kumar, Rensselaer Polytechnic Institute Murat Yuksel, University of Nevada, Reno Aparna Gupta, Rensselaer Polytechnic Institute Koushik Kar, Rensselaer Polytechnic Institute Industrial Engineering Research Conference, IERC, 61st Annual Conference & Expo, Reno, Nevada, May 21-25, 2011. 1

2 Overview Introduction Problem Formulation – Network Model – Single Edge – to – Edge Contract Link – Multiple Edge – to – Edge Contract Links Simulation Experiments – Single Edge – to – Edge Link – Multiple Edge – to – Edge Links Network Setup Results Conclusion and Future Work 2

3 Introduction Migration of data rich applications indicate that the increasing demand for bandwidth is unlikely to vanish Managing bandwidth allocation to customers presents a major challenge and received a lot of attention Many approaches involving advanced techniques for achieving efficient contracting among ISPs have been explored 3

4 Introduction (contd..) Crucial handicap of existing inter – ISP economics is coarse granularity of contracts Precursor to realization of more dynamic & automated contracting is sufficient motivation for ISPs to invest and install necessary tools & protocols Question focused on : how much benefit there is if the contracts were classified into simple two market regime, highly dynamic and long time scale durable 4

5 Introduction (contd..) Also, an important issue is the management of risks in ISPs investments Though an automated and dynamic way of establishing contracts will enable ISPs to flexibly allocate their resources several questions arise: – When and where to advertise a new contract – How to assess the risks involved and reflect them on contracting parameters – How to divide links between different types of contracts 5

6 Introduction (contd..) Model and formulate revenue maximization problem by considering the constraints imposed by correlation among contract links Through a detailed simulation several insights on optimal reservation levels for the two types of contracts are provided 6

7 Problem formulation Contract switched Internet architecture considered 7

8 Problem formulation(contd..) Edge – to – edge contract links are advertisable contracts between pairs of ingress and egress routers The Capacity of a contract link is equal to the minimum of the capacities of the physical links used to construct the contract link 8

9 Problem formulation(contd..) Capacity of the contract links must be segmented between short – term and long – term contracts There is enough demand for long – term contracts while the demand for short – term contracts is stochastic Revenue from unit demand for short – term contracts per unit time is higher than Revenue from unit demand for long – term contracts per unit time 9

10 Single Edge- to- Edge Contract Link Single edge – to – edge contract link is considered Enough demand for long – term contracts is assumed. Bandwidth allotted for long – term contracts is always used Bandwidth available for short – term contracts may or may not be used ISP must choose optimal value for bandwidth reserved towards long – term contracts so that total expected revenue per unit time is maximized 10

11 Single Edge- to- Edge Contract Link(contd..) 11 Revenue obtained from long - term contracts per unit time Revenue obtained from short – term contracts per unit time Bandwidth reserved for long – term contracts Capacity of the contract link

12 Multiple Edge- to- Edge Links Assuming several edge – to – edge links in ISPs network Edge – to – edge contract links share physical links which impose constraints on bandwidth reserved for long – term contracts of these contract links 12

13 Multiple Edge- to- Edge Links (contd..) Bandwidth reserved for long – term contracts Capacity of the physical links used to construct the contract link 13

14 = Revenue form long term contracts per unit time Multiple Edge- to- Edge Links (contd..) Revenue from long –term contracts per unit time Sum of revenue obtained from long term contracts on multiple contract links 14

15 Multiple Edge- to- Edge Links (contd..) Revenue obtained from short term contracts per unit time Minimum of Short term demand and residual capacity Reservation for short – term contracts on contract link Reservation for short – term contracts on a contract link Remaining capacity of physical link l 15

16 Multiple Edge- to- Edge Links (contd..) 16 Revenue from long – term contracts per unit time Revenue from short – term contracts per unit time Bandwidth reserved towards long term contracts Capacity of contract link Bandwidth reserved for long – term contracts Capacity of the physical links used to construct the contract link

17 Simulation Experiments Single edge – to – edge link Capacity of single edge – to – edge contract link is set to 10 Two distributions of short term demand – Uniform distribution between 0 and 10 – Truncated Gaussian distribution in the interval [0,10] with mean 5 and standard deviation 1 17

18 Simulation Experiments(contd..) 18 Optimal long term reservation on contract link is shown. P L is increased from 0 and P S is fixed at 10 Optimal long term reservation on contract link on Y- axis P L is being increased from 0 to 10 on X-axis Graph for truncated Gaussian distribution Graph for uniform distribution

19 Multiple Edge – to – Edge Links Network Setup Real topology map of GEANT ISP with 23 routers is used. 3 ingress & 3 egress routers are chosen. Nine edge- to- edge contract links are considered Capacity of physical links is set to 10 and so the capacity of contract link (B ie ) is 10. Short term demand is taken to be uniformly distributed between 0 and 10 19

20 Results Maximum total revenue, maximum long term revenue and maximum short term revenue are shown. P S is set to 10 20 P L is being increased from 1 to 10 on X-axis Maximum total revenue on Y- axis P L 5 then P L is not high enough to generate significant long term revenue P L >5 then long – term contracts generate higher revenue

21 Optimal reservation levels on nine links when P L =5 and P S =10 & P L =10 and P S =10 Results(contd..) 21 P L =5 and P S =10 P L =10 and P S =10 Contract links listed on X- axis Contract link capacity on Y-axis Contract links listed on X- axis Contract link capacity on Y-axis Increasing P L increases long - term reservation levels Long term reservation levels on 1,6,7 remain zero at all times

22 Conclusion and Future Work Revenue maximization problem was formulated for an ISP which wants to participate in two segments of bandwidth markets The level and distributional characteristics of short – term demand and interactions among contract links are key determinants In the future, possibility of multiple paths underlying a given single contract link instead of single path will be considered 22

23 Queries? 23


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