1. Performance Optimization of Single-Cell Voice over WiFi Communications Using Quantitative Cross-Layering Analysis Fabrizio Granelli(UniTN) Dzmitry Kliazovich(UniTN) Jie Hui(Intel Corp.) Michael Devetsikiotis(NCSU) June 19th, 2007

2. 2 Motivation  Layering  Enable fast development of interoperable systems, but…  … limited the performance of the overall architecture, due to the lack of coordination among protocols  Cross-Layering  A novel design principle, whose idea is to allow coordination, interaction and joint design of protocols crossing different layers  Seems appropriate for specific scenarios, such as wireless, where independent layer design may be sub-optimal  No formal (quantitative) characterization of the cross-layer interaction among different levels of the protocol stack is available yet

3. 3 Objectives and Contribution  Objectives  to identify and formalize the interactions crossing the layers of the standardized protocol stack;  to systematically study cross-layer effects in terms of quantitative models;  to support the design of cross-layering techniques for optimizing network performance;  to define design principles of Call Admission Control (CAC) strategies  Contribution  a general quantitative approach  methodological contribution: adopt “metamodeling”  a case study of VoWiFi cell: VoIP capacity and operator revenues optimization

4. 4 Protocol Design Issues  Layering (ISO/OSI)  It is possible to model the ISO/OSI layer N entity as an object characterized  parameters of the object, p N  measurements that it can perform, m N  Cross-Layering  Weak Cross-Layering  interaction among layers of the protocol stack  includes “non-adjacent” interactions  Strong Cross-Layering  allows joint design of the algorithms within any entity at any level of the protocol stack  individual features related to the different layers can be lost due to the cross-layering optimization

5. 5 Quantifying Cross-Layering  Quantifying the effect of potential cross-layer interactions is very important  to systematically relate such interactions to system outcomes  to quantify the decision to take such interactions into account  We propose to quantify cross layer interactions by defining factors (parameters) and effects (measurements) across layers  in a way that is common in system science and operations research

6. 6 Quantifying Cross-Layering (cont.)  A system is characterized by  “factors” (controllable parameters)  “effects” (performance metrics)  The sensitivity of the system response and interactions can be captured using partial derivatives:

7. 7 Quantifying Cross-Layering (cont.)  Using such tools, it is possible to optimize the performance e i with respect to a subset of p TOT under general constraints  by using steepest ascent, stochastic approximation, ridge analysis, stationary points, etc.  or to make local steps or decisions at a given operating point  in the context of game-theoretic or other economic-driven adjustments  or one may wish to dynamically control the response f k over time (optimal control)

8. 8 Quantifying Cross-Layering (cont.)  The quantitative degree of cross-layer interaction and sensitivity will also guide one to a decision of whether to actually take a specific interaction into account or not  cross layer designs have implicit disadvantages in terms of cost and complexity  Some researchers have underlined that cross- layer design should be considered under a cautionary perspective [*]  a concept that our proposed framework integrates and rationalizes. [*]V. Kawada, and P.R. Kumar, “A Cautionary Perspective on Cross- Layer Design,” IEEE Wireless Communications, Vol. 12, No. 1, pp. 3-11, Feb. 2005.

9. 9 Economic Considerations  Utility  “raw” performance metrics e i will typically be further incorporated into utility functions U(e)  express better how valuable the performance metric is to the system owner or user  examples include functions of the system throughput, overall delay or jitter, and system capacity  the utility function can have several forms and shapes  Prices  controllable parameters (factors or resources) will also likely to have actual (literal) or virtual prices, say $a per unit of design parameter X and $b per unit of Y

10. 10 System Design Issues  System Design & Optimization  analytical, numerical or simulation-based methods could be used to achieve the design goals, either up front (i.e., parameter optimization), or on-line (i.e., optimal control)  More in detail, by employing the proposed framework, it is possible to select:  the optimal operating point of the system (direct consequence of the optimization process);  the proper cross-layer interactions to enable (based on sensitivity of the system);  the proper signaling architecture to employ (allowing to identify the set of parameters and measurements to use).

11. 11 Case Study: VoWiFi Capacity  Network Model  Problem Statement: maximum # of VoIP calls, supported in an infrastructure Wi-Fi, with satisfactory QoS performance  Network Model: Infrastructure, N stations APP: G711 VoIP 64 Kb/s RTP/UDP/IP: header MAC: DCF with no RTS/CTS PHY: 802.11b, 11Mbps  Cross-Layer interactions: Between PHY, MAC, and APP

12. 12 Case Study: VoWiFi Capacity  Inputs  X=[ DataRate ErrorRate NumofRetr VoicePktIntvl ]  Outputs  maximum # of VoIP calls supported by WLAN cell Y = N* with satisfactory quality  Constrains  Objective: acceptable voice quality (MOS = 3)  End-to-end delay measured between unpacketized voice: < 100 ms  Voice frame error rate: < 5%  Design Parameters

13. 13 Case Study: VoWiFi Capacity (cont.)  Choose and Fit the Metamodel  Second order polynomial RSM with interactions (R 2 =0.81)  Evaluate the Metamodel: comparison  Analysis > Metamodel > Simulation 0 2 4 6 8 10 12 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 30 35 40 VoIP Capacity N * DataTxRate (Mbps) VoicePktIntvl (ms) Analysis Metamodel Simulation

14. 14 Case Study: VoWiFi Capacity (cont.)  Metamodel properties  Maximum of N*(D, I, R, PER) corresponds to  20 VoIP calls for D=11 Mb/s, I=70 ms, R=5, PER=10 -9 Violates E2E delay threshold of 100 ms For low rates (1 or 2 Mb/s) further retransmissions start to degrade system performance Model is not sensitive to low PERs

15. 15 Case Study: VoWiFi Capacity (cont.)  Cross-Layer Sensitivity and Performance Optimization  System is sensitive  Voice packet interval (I) and Packet Error Rate (PER)  System is less sensitive  Data rate (D) and Number of MAC layer retransmissions (R)

16. 16 Case Study: VoWiFi Capacity (cont.)  Service Provider Perspective  Utility function: P call - Price charged for a single call P power - Marginal cost of a unit of transmitted power D wasted - Bandwidth wasted for retransmission in packets/second P call / P power - chosen to be equal to 100 which corresponds to a policy to charge $1 per VoIP call while the price paid for power resouce is just ¢1  Maximum revenues:  $18.89 with D=11 Mb/s, I=70 ms, R=5, PER=10 -9 Operator revenues on per-call basis Resources required by retransmissions Resources required to maintain a certain data and error rates

17. 17 Case Study: VoWiFi Capacity (cont.)  Mobile Terminal Perspective  Objective: long battery life while providing acceptable call performance  Main parameters  transmission data rate D  maximum number of retransmissions R  Utility function: where and relative weight against costs

18. 18 Case Study: VoWiFi Capacity (cont.)  Design Principles  limitation on the number of active nodes, and thus a proper Call Admission Control (CAC), is required  overall system performance depend on many parameters which can be recognized and quantified at different layers  This motivates an introduction of CAC schemes which exploit metamodel information to provide proper cross-layer parameter setting for run-time system optimization

19. 19 Conclusions  A general formal framework for  analyzing and quantifying cross-layer interactions and  supporting the design of cross-layering techniques to optimize network performance, including cost-benefit considerations  A case study from IEEE 802.11 VoIP is analyzed  From VoIP capacity, network operator and mobile terminal perspectives  Ongoing work  to test the proposed framework in more complex scenario  to provide guidelines in definition of high-performance cross-layering solutions  to design metamodel-based Call Admission Control (CAC) approaches

20. Thank you!