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Robert Schafrik Lakshman Krishnamurthy

Bandwidth Reallocation for Bandwidth Asymmetry Wireless Networks Based on Distributed Multiservice Admission Control. Robert Schafrik Lakshman Krishnamurthy. Agenda. Introduction Related work on Admission control and bandwidth allocation Distributed Multiservice Admission Control

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Robert Schafrik Lakshman Krishnamurthy

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  1. Bandwidth Reallocation for BandwidthAsymmetry Wireless Networks Based onDistributed Multiservice Admission Control Robert Schafrik Lakshman Krishnamurthy

  2. Agenda • Introduction • Related work on Admission control and bandwidth allocation • Distributed Multiservice Admission Control • System model & DMS-AC in Two-cell system • DMS-AC in Multi-cell system • Performance evaluation • Competing Systems • Static Allocations • Conclusion • Comments

  3. Introduction Next generation multiservice wireless networks are expected to present distinctive traffic asymmetry between uplink and downlink. Some resources may be wasted if bandwidth is allocated symmetrically To match the asymmetric traffic load, it is necessary to allocate different bandwidth to uplink and downlink. Different call classes have different up/down ratios QoS may be different for Handoff and new call, and for each call class

  4. Introduction (continued) If the traffic and mobility patterns are predictable, then fixed bandwidth allocation works. Bursty and variable bandwidth requirements call for new treatments of network resource management Traffic generated is time dependent It is necessary to develop a dynamic bandwidth allocation scheme that can adapt to the changing traffic conditions

  5. Problem Statement • Upload and Download communications are not always symmetric • Need to determine under what conditions bandwidth needs to be reallocated • Need to determine the best way to reallocation when multiple call classes and multiple cells while preserving QoS

  6. Time Slots • Some timeslots are for uplink, some are for downlink. This prevents collisions • Variable time-slots for different cells always outperforms fixed time slots • Reallocation of time slots affects all calls in the system, try to limit how frequently this is done

  7. QoS Metric • Call Admission Control (CAC) • Critical CAC Parameters • Pn – New call blocking probability • Ph – Handoff call blocking probability • MINBlock used to optimize

  8. Distributed Multiservice Admission Control (DMS-AC) • Provides a base to compare new techniques against • Tries to find proper threshold • Limits new calls of certain classes • If blocking probability exceeds a bound, it reallocates • If QoS thresholds for some classes cannot be found, it reallocates

  9. Related Work • CAC schemes • CDMA (fixed, symmetric) • CDMA/TDD (fixed, asymmetric) • SA – same-slot allocation (all cells have same allocation) • DA – different slot allocation (cells can have different allocations, but adjacent cells may have slot interference) • Limited Fractional Guard Channel scheme • DCA Distributed Admission Control • Jeon’s CAC for MSWN [7] • DMS-AC scheme

  10. Limited Fractional Guard Channel (LFGC) • Minimize a linear objective function • Weighted sum of handoff and new call blocking probabilities • C channels • C-T reserved for new and handoff • When T channels are used, only handoff calls are accepted • Extended to deal with multiple call classes[20]

  11. Distributed Admission Control (DCA) • Based on communication between cells to predict handoffs • Only deals with one call class • Knapsack problem [18] to deal with multiple call classes

  12. Distributed Multiservice Admission ControlSystem Model • Total bandwidth allocated of a cell is fixed. • Bandwidth allocated on uplink and downlink is different and also adjustable [3] [8] • M classes of calls in the system • The calls of particular class have the same bandwidth requirements, mobility characteristics and mean resource holding time

  13. Distributed Multiservice Admission ControlSystem Model (contined) • Design goal of the proposed admission control scheme is: φi < ηi Φi < ρi • ηi (eta)- Highest tolerable dropping probability of class i hand-off calls. • φi (phi) – hand-off call dropping probability of class i calls • Φi (phi) – New call blocking probability of class i calls. • ρi (rho) – Highest tolerable new call blocking probability

  14. Distributed Multiservice Admission ControlSystem Model (contined) • DMS-AC operates in distributed manner • System states exchanged periodically between adjacent cells • Base station of cell makes an admission decision based on the state information of the cell itself and its neighboring cells. • DMS-AC uses the admission threshold of each call class based on the system states to limit the admission of new calls. • Dynamic threshold scheme is used. • Threshold of specific call class is recomputed and reset periodically. • Control period – interval between two threshold computing process (15 – 60 minutes).

  15. Distributed Multiservice Admission control in a Two-Cell System Fig. 1. Two-cell system. • Cr is the observing cell and Cl is the neighbouring cell • Total bandwidth in Cr (Cl) is denoted by Bru + Brd (Blu + Bld) • In DMS-AC we need to define the overload states of a specific call class in the multiservice system. • In multiservice networks, the set of overload states of different call classes may be different.

  16. Distributed Multiservice Admission control in a Two-Cell System (contined) • Example: • Cell has 10 downlink and 5 uplink channels • Class 1 calls require 1 uplink and 1 downlink channel • Class 2 calls require 1 uplink and 3 downlink channels • (n1, n2) denote the system states, where n1 and n2 denote the class 1 calls and class 2 calls in the system • (0,3) and (2,2) are overload states of class 2 calls. No class 2 calls are not admissible while class 1 calls are admissible. Fig. 2. An example. (a) Overload states of class 1 calls. (b) Overload states of class 2 calls

  17. Distributed Multiservice Admission control in a Two-Cell System (contined) • During a control period, the admission of class i new call in the observing cell Cr should satisfy the following two conditions: • The admission of a new class i call in Cr cannot cause the call dropping probability of class j call in Cr denoted by φrj to exceed ηj • The admission of a new class I call in Cr cannot cause the call dropping probability of call class j in the neighboring cell Cl, denoted by φlj to exceed ηj • The key of DMS-AC is to determine the thresholds of individual call class in each cell (i.e. we need to compute φrj and φlj)

  18. Distributed Multiservice Admission control in a Two-Cell System (continued) • The key of DMS-AC is to determine the thresholds of individual call class in each cell (i.e. we need to compute φrj and φlj) • The probability that xi class i calls out of ri calls stay in Cr has a binomial distribution given by • Similarly, the probability that yi class i calls handoff to Cr from Cl during the control period is

  19. Distributed Multiservice Admission control in a Two-Cell System (continued) • Using formulas 1 & 2, we need can find Pr(ni) • Pr(ni) denote the probability that there are ni class i calls in Cr during T units of time • At any time system stays in feasible state, should satisfy

  20. Distributed Multiservice Admission control in a Two-Cell System (continued)

  21. Distributed Multiservice Admission control in a Two-Cell System (continued) Blocking probability of class j calls in Cr can be expressed as: Blocking probability of class j calls in Cl is expressed as:

  22. Derivation of Admission threshold Thi1 and Thi2 denote the thresholds of class i calls that satisfies the first and second admission conditions The final admission threshold of class i calls in Cr, which satisfies all admission conditions , is given by

  23. Extension to multicell system C0 be the current observing cell C1 to C6 be the neighboring cell

  24. Extension to multicell system During a control period, the admission of a class i (I Є [0, M-1]) call in C0 should satisfy: The admission of a new class i call in C0 cannot cause the call dropping probability of call class j in C0, φ0j to exceed ηj The admission of a new class i call in C0 cannot the call dropping probability of call class j in the neighboring cells to exceed ηj

  25. Valid States q n • Number of calls for class q • in the system M Number of feasible states Constrained by Bu and Bd Call classes Not all of these states are good for the system, but they are possible. Matrix will not be symmetric. S(i,j) is the subset of states such that adding a call of type i will cause overload for class j

  26. Threshold-Based Admission Control Scheme If you are the current call class (note: not always zero!) Test for conditions 1,2, and 5 You are NOT the current cell Conditions

  27. Case 1 – Cell i will Become Full for Some Call Class si is in the set S(i,j) – adding a call type i will cause at least one other class j to become full Need to reallocate up/down channels

  28. Admission Case 1 – Ratio of Uplink and Downlink Needs to Change Need to choose an allocation between

  29. Admission Case 2 – Cell r Will Not be able to Accept Handoff from Cell l Cell r either doesn’t have enough room or accepting a handoff will cause a class to overload See if Cell r can reallocate to accommodate

  30. DMS-AC Pseudocode

  31. Comparisons • Analysis using a 2-cell system • 15 minute control period • 100 channels • 2 call classes • Real time ( 1 up, 1 down ) • Non Real Time ( 1 up, 3 down )

  32. Jeon’s scheme Similar goal – create a scheme for reallocating in asymmetric environments Accounts for traffic load in both directions Uses Markov analysis Also only considers QoS for New and Handoff calls

  33. Comparison with Jeon (1) New call QoS is similar, and not shown Jeon does not consider NRT QoS

  34. Comparison with Jeon (2) Call types vary independently

  35. Comparison with Jeon (3) Similar performance with small loads Jeon’s begins to lag with NRT calls Jeon’s breaks down when volume is high

  36. Comparison with Static Sol’n (1) No reallocation is performed for “AC without BA” RT call arrival rates in both Cr and Cl increase from 0.07 to 0.12 simultaneously Up/down ratio is 30 up/ 70 down

  37. Comparison with Static Sol’n (2) average NRT call arrival rates in both Cr and Cl change from 0.006 to 0.011 simultaneously Up/down ratio is initially 50 up/ 50 down

  38. Comparison with Static Sol’n (3) Traffic increases for Cr, decreases for Cl

  39. Conclusions • Changing the up/down ratio for several asymmetric call classes helps maximize the resources of a Cell, and still guarantees QoS for new and handoff calls • When to reallocate • Allocations for a call class nears max • Allocations of neighbor for that class nears max • How to reallocate • Find min B that fills QoS requirement

  40. Comments Experimental setup was simplistic Perhaps more than 2 call types could be considered in a simulation Perhaps compare the performance of more than 2 cells A call cannot itself be dynamic (aka use 1up/1down for a while then switch to 1up/2down) Does not consider revenue, but that might be achievable by adjusting admittance thresholds Performs slightly better than Jeon in some conditions

  41. Backup Slides

  42. Other Notes Assume C0 covers a conference, and becomes overloaded C1 - C6 will be unable to accept any calls of any class (due to the handoff constraint) C0

  43. Overview • Uplink and Downlink bandwidth is asymmetric • Determine when to change ratio of uplink to downlink • Determine how to compute best ratio to satisfy QoS • Satisfy QoS for call classes

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