1 / 27

Delay Tolerant Voice Calls in a GSM/GPRS Cell

University of Glamorgan Mobile Computing and Networking Research Centre. Performance Modelling with MOSEL-2. Delay Tolerant Voice Calls in a GSM/GPRS Cell. Patrick Wuechner 1,2 , Khalid Al-Begain 1 , Joerg Barner 2 , Gunter Bolch 2 1 University of Glamorgan, Wales, UK

jabir
Download Presentation

Delay Tolerant Voice Calls in a GSM/GPRS Cell

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. University of Glamorgan Mobile Computing and Networking Research Centre Performance Modelling with MOSEL-2 Delay Tolerant Voice Calls in a GSM/GPRS Cell Patrick Wuechner 1,2, Khalid Al-Begain 1, Joerg Barner 2, Gunter Bolch 2 1 University of Glamorgan, Wales, UK 2 University of Erlangen, Germany

  2. Outline • Introduction • Performance Modelling with MOSEL-2 • A single GSM/GPRS Cell with Delay Tolerant Voice Calls (DeTVoC) • Recent Improvements of MOSEL-2

  3. Introduction • MOSEL (Modelling, Specification, and Evaluation Language) is a textual high-level modellinglanguage. • Developed in Erlangen. • Suitable for describing system model based on queuing networks, Petri Nets, and many else. • MOSEL Package contains: Language Interpreter, Numerical/simulation solvers, and graphical tool for result presentation.

  4. Introduction History of MOSEL • MOSLANG (predecessor of MOSEL) • MOSEL (H. Herold, G. Bolch, K.Al-Begain) • Markov solution only • C based syntax and support • Support for SPNP and MOSES tools • MOSEL-2 (B. Beutel, J. Barner, G.Bolch) • Markov and DSPN based solution (immediate, exponential and deterministic transitions) • Simulation • Moved away from C • Support for SPNP and MOSES tools • Next MOSEL (P. Wuechner, K.Al-Begain) • Empirical and General distributions • ???

  5. MOSEL-2: A Basic Example • Closed Tandem Network: • Petri Net model description: Exp Det p2 p1 N=3

  6. MOSEL-2: A Basic Example • Closed Tandem Network: • MOSEL model description: Exp Det p2 p1 N=3 1: PARAMETER rate_t1 := 2 .. 8 STEP 2; 2: CONST mean_t2 := 1/5; 3: NODE p1[3] := 2; 4: NODE p2[3] := 1; 5: FROM p1 TO p2 RATE rate_t1; 6: FROM p2 TO p1 AFTER mean_t2; 7: PRINT avg_in_p1 := MEAN(p1);:

  7. MOSEL-2: A Basic Example • Closed Tandem Network: • MOSEL model description: Exp Det p2 p1 N=3 1: PARAMETER rate_t1 := 2 .. 8 STEP 2; 2: CONST mean_t2 := 1/5; 3: NODE p1[3] := 2; 4: NODE p2[3] := 1; 5: FROM p1 TO p2 RATE rate_t1; 6: FROM p2 TO p1 AFTER mean_t2; 7: PRINT avg_in_p1 := MEAN(p1);: Parameter Part

  8. MOSEL-2: A Basic Example • Closed Tandem Network: • MOSEL model description: Exp Det p2 p1 N=3 1: PARAMETER rate_t1 := 2 .. 8 STEP 2; 2: CONST mean_t2 := 1/5; 3: NODE p1[3] := 2; 4: NODE p2[3] := 1; 5: FROM p1 TO p2 RATE rate_t1; 6: FROM p2 TO p1 AFTER mean_t2; 7: PRINT avg_in_p1 := MEAN(p1);: System State Definition Part

  9. MOSEL-2: A Basic Example • Closed Tandem Network: • MOSEL model description: Exp Det p2 p1 N=3 1: PARAMETER rate_t1 := 2 .. 8 STEP 2; 2: CONST mean_t2 := 1/5; 3: NODE p1[3] := 2; 4: NODE p2[3] := 1; 5: FROM p1 TO p2 RATE rate_t1; 6: FROM p2 TO p1 AFTER mean_t2; 7: PRINT avg_in_p1 := MEAN(p1);: Transition Rules Part

  10. MOSEL-2: A Basic Example • Closed Tandem Network: • MOSEL model description: Exp Det p2 p1 N=3 1: PARAMETER rate_t1 := 2 .. 8 STEP 2; 2: CONST mean_t2 := 1/5; 3: NODE p1[3] := 2; 4: NODE p2[3] := 1; 5: FROM p1 TO p2 RATE rate_t1; 6: FROM p2 TO p1 AFTER mean_t2; 7: PRINT avg_in_p1 := MEAN(p1);: Result definition part

  11. MOSEL-2: Invoking the Environment • Command line: • Options: -T: Translate MOSEL model to TimeNET model. (can be c for SPNP or m for Moses) -s: Start appropriate tool (TimeNET) automatically. mosel2 –Ts tandem.mos

  12. MOSEL-2: Workflow real system • building MOSEL-2 system description and starting the environment • MOSEL-2 translates the given model to the input file format of an evaluation tool • the appropriate tool is called by MOSEL-2 • the tool evaluates the model either using numerical analysis methods or discrete event simulation • tool writes it output to tool specific output files • MOSEL-2 collects and parses all output files to uniform result files user 1 1 sys.mos 2 2 sys.m sys.c sys.tn … sys.? 3 3 MOSES SPNP TimeNET … other 4 4 discrete event simulation numerical analysis 5 5 tool specific result … files 6 6 sys.{res, igl}

  13. MOSEL-2: Result Presentation • graphical:(if specified) • textual: Stationary analysis of "tandem.mos" by TimeNET Parameters: rate_t1 = 2 Results: avg_in_p1 = 2.49505 ============================================== Parameters: rate_t1 = 4 Results: avg_in_p1 = 1.80837 ============================================== Parameters: rate_t1 = 6 Results: avg_in_p1 = 1.20243 ============================================== Parameters: rate_t1 = 8 Results: avg_in_p1 = 0.818239

  14. GSM/GPRS Cell with Delay Tolerant Voice Calls • Assumptions: • N GSM channels shared between Voice and data traffic. • Voice is circuit-switched. • Data is packet switched (can be buffered). • Data traffic utilises all channels not used by Voice. • When all channels are busy Voice calls are blocked but NOT lost immediately. • Blocked voice calls are delayed for a certain (fixed) amount of time before being rejected.

  15. DSPN model of GSM/GPRS air interface t1: Poisson voice arrival processt2: Poisson data arrival processt3: data losst4: data admissiont5: call admissiont6: DeTVoC (deterministic, IS)t7: data service (exponential, IS)t8: voice service (exponential) D V t1 t2 t3 p1 p2 t7 TDMA t4 p3 t5 t8 p4 t6 … D V

  16. MOSEL-2 Model 01/* PARAMETER AND CONSTANTS **********************/ 02 PARAMETER lambda_d := 0.1, 0.5 .. 2.5 STEP 0.25; 03 CONST lambda_v := 1/100; 04 CONST mue_single_v := 1/80; 05 CONST max_users_v := 100; 06 CONST chan_res_d := 1; 07 CONST waiting_time := 10; 08 CONST mue_single_d := (21.4/8) / 10; 09 10/* NODES ****************************************/ 11 NODE p1[max_users_v] := 0; 12 NODE p2[1] := 0; 13 NODE p3[32] := 0; 14 NODE p4[chans-chan_res_d] := 0; 15 16/* TRANSITIONS **********************************/ 17 FROM EXTERN TO p1 RATE lambda_v; //t1 18 FROM EXTERN TO p2 RATE lambda_d; //t2 19 FROM p2 TO EXTERN PRIO 1; //t3 20 FROM p2 TO p3 PRIO 2; //t4 21 FROM p1 TO p4 PRIO 2; //t5 22 @<1..max_users_v>{ //t6 23 IF p1 >= # FROM p1 24 TO EXTERN 25 AFTER waiting_time 26 PRIO 1; 27 } 28 FROM p3 TO EXTERN RATE mue_single_d * (8-p4); //t7 29 FROM p4 TO EXTERN RATE mue_single_v * p4; //t8 30 31/* RESULTS **************************************/ 32// TEXTUAL (.res) 33 PRINT d_loss := PROB (p3=32); 34 PRINT v_block := UTIL (p1); 35 36// GRAPHICAL (.igl) 37 PICTURE “prob. of data loss and voice blocking" 38 PARAMETER lambda_d 39 XLABEL "incoming burst rate" 40 YLABEL "PROB" 41 CURVE d_loss 42 CURVE v_block

  17. MOSEL-2 Results

  18. MOSEL-2 Results

  19. MOSEL-2: Recent Improvements • Introducing automatic approximation of “empirical” distributions: • MOSEL: • Petri Net: • 3 different approximations depending on Squared Coefficient of Variation:

  20. MOSEL-2: Recent Improvements • c2 = 1Empirical distribution gets substituted by Exponential distribution with service rate:

  21. MOSEL-2 Recent Improvements • c2 > 1Empirical distribution gets substituted by Generalized Exponential (GE) construct:

  22. MOSEL-2: Recent Improvements • c2 < 1Empirical distribution gets substituted by Hypoexponential Phase-Type (PH) construct:

  23. MOSEL-2: Application revisited • ESPN model of GSM/GPRS air interface: t1: Poisson voice arrival processt2: Poisson data arrival processt3: data losst4: data admissiont5: call admissiont6: DeTVoC (exp delay)t7: data service (empirical, IS)t8: voice service (exponential) t3 D V t7 t1 t2 p1 p2 TDMA t4 p3 t5 t8 p4 t6 … D V

  24. MOSEL-2 Model 1/* PARAMETER AND CONSTANTS **********************/ 2CONSTlambda_d := 1.5; 3PARAMETER lambda_v := 0.02 .. 0.06 STEP 0.01; 4 CONST mue_single_v := 1/80; 5 CONST max_users_v := 50; 6 CONST chans := 8; 7 CONST chan_res_d := 1; 8 CONST chan_max_d := chans - 2; 9 CONST waiting_time := 10; 10 CONST mean_single_d := 10 / (21.4/8); 11PARAMETER scv_d := 0.5, 1, 2; 12 CONST var_d := scv_d * mean_single_d 13 * mean_single_d; 14 15/* NODES ****************************************/ 16 NODE p1[max_users_v] := 0; 17 NODE p2[1] := 0; 18 NODE p3[32] := 0; 19 NODE p4[chans-chan_res_d] := 0; 20 21/* TRANSITIONS **********************************/ 22 FROM EXTERN TO p1 RATE lambda_v; //t1 23 FROM EXTERN TO p2 RATE lambda_d; //t2 24 FROM p2 TO EXTERN PRIO 1; //t3 25 FROM p2 TO p3 PRIO 2; //t4 26 FROM p1 TO p4 PRIO 2; //t5 27 FROM p1 TO EXTERN RATE //t6 28 p1*(1/WAITING_TIME) PRIO 1; 29 30 31 @<1..chan_max_d>{ //t7 32 IF (chans-p4) >= # 33FROM p3 TO EXTERN EMP (mean_single_d, var_d); 34 } 35 FROM p4 TO EXTERN RATE mue_single_v * p4; //t8 36 37/* RESULTS **************************************/ 38// TEXTUAL (.res) 39 PRINT d_loss := PROB (p3=32); 40 PRINT v_block := UTIL (p1); 41 42 PRINT data_mean := MEAN (p3); 43 PRINT voice_mean := MEAN (p4); 44 PRINT voice_mean_w := MEAN (p1); 45 46// GRAPHICAL (.igl) 47 48 PICTURE "data burst loss and voice blocking" 49 PARAMETER lambda_V 50 XLABEL "incoming burst rate" 51 YLABEL "PROB" 52 CURVE d_loss 53 54 55 PICTURE “Voice Blocking Probability" 56 PARAMETER lambda_v 57 XLABEL "incoming burst rate" 58 YLABEL “PROB" 59 CURVE d_loss 60 61

  25. MOSEL-2: Results 1

  26. MOSEL-2: Results 2

  27. Conclusion • MOSEL is growing and getting more and more interesting features • Increased modelling power • Unfortunately, the new features limit the size of the model because of new nodes added. • We hope to start a new project on a new version of MOSEL that will be very powerful tool with many new features.

More Related