NOBEL – Munich meeting, June 2005

1. NOBEL – Munich meeting, June 2005 WP2 A2.2 QoS/Route Management Contribution from Telenor (Partner #27) Heidi Kjønsberg, Inge Einar Svinnset NOBEL München meeting, June 2005

2. How to use measurements for resource reservationPlans 2005 (presented at Madrid meeting) • Specify true real-time algorithm • Threshold values for when to reallocate resources • Study non-stationary synthetic traces • Underlying trends superpositioned on Poisson / Weibull • Analyse how different components of measured traffic traces behave for the real-time resource allocation algorithms • In aggregate traffic the traffic mixture that accidentally happens to be present determines the overall traffic characteristics • The interesting part is to study effects that are not accidental • Make connection to flow-level traffic models and effective bandwidths NOBEL München meeting, June 2005

3. Traffic trace used for initial studies of non-stationarity • Packet IAT distribution: • Short time scale: exponentially distributed IATswith mean_IAT = 0.13 ms • Long time scale: sine curve • Packet lengths: • 0.2*mean_IAT • Load: • sine curve that oscillates between 0.1*0.2 and 1.9*0.2, with a mean 0.2 and period T=5 s • Number of packets in trace: 10^5 • Per 10 ms, on average 77 packet arrivals • The whole trace takes approximately 13 sec • Mathematical expression, probability that at time t the packet IAT is <= tau: c = 1/(0.13 ms); T = 5 sec NOBEL München meeting, June 2005

4. Resource reservation algorithm Incoming traffic stream Outgoing, shaped traffic stream • For each simulation fix • Tres alloc = time interval used for measurements • B = buffer size • Update algorithm for dynamic output rate • Properties: • No feedback in algorithm (packet loss feedback would be one possibility) • Buffer size is not varied dynamically Measure-ments Dropped packets i = measurement interval indexL(i) = average load in interval i Other update algorithms will be studied. NOBEL München meeting, June 2005

5. Evaluation criteria for resource reservation algorithm • Metrics used for evaluation • Packet loss • Average packet loss over long time scales (the whole packet trace) • Average packet loss over small time scales (parameter Tloss is set to 10 ms) • Bandwidth waste • Average bandwidth waste over time scales corresponding to resource allocation update algorithm • Time scales involved • 5 sec, the scale of long term traffic variations • Tloss = 10 ms, the time scale of the requirement on small time scale packet loss • Tres alloc, the time scale used for resource allocation, • Goal of resource allocation algorithm (rough formulation) • Keep overall packet loss probability low • Keep loss at small time scales reasonably low most of the time • Keep bandwidth waste low What is the best parameter settings of the algorithm, given specified QoS requirements and limits for resource waste? How strict QoS requirements can be handled by the algorithm? NOBEL München meeting, June 2005

6. Packet loss at small time scales • Formulation of QoS requirement for packet loss at small time scales: • Our choice for initial analysis: • Tloss = 10 ms • x= 5; 1; 0.5; 0.05 • When average packet loss is measured over non-overlapping time intervals of length Tloss, the fraction of intervals with packet loss <= x % must be > Z % NOBEL München meeting, June 2005

7. Results – overall packet loss Specification of overall packet loss requirements • For VoIP overall loss must be <= 5% • The other QoS class, overall loss must be <= 0.5% Conclusions based on figure • For VoIP the requirement can be met if parameters are chosen to be Tres alloc = 0.1 s, alpha = 0 or alpha = 0.5 • The loss requirement of the other QoS class cannot be met unless we decrease Tres alloc below 0.1 sec 5% 0.5% Overall loss probability = # packets lost/# packets offered Comment on time scales: Tres alloc /period_of_sine = Tres alloc /5s => 0.1/5 = 0.02.If traffic period was instead 1 day, the resource allocation time scale satisfying the same ratio would be 0.02*24*60 min = 28.8 minutes NOBEL München meeting, June 2005

8. Results – packet loss at small time scales For Tres alloc = 0.1 = 0.02* traffic_period: For alpha <= 0.5 no more than 23% of small time scale intervals experience packet loss higher than any of the investigated thresholds (5%, 1%, 0.5%). For alpha =0 the corresponding number is 8% For Tres alloc = 1.0 = 0.2* traffic_period: Between 43% and 50% of small time scale intervals experience packet loss higher than any of the investigated thresholds (5%, 1%, 0.5%) NOBEL München meeting, June 2005

9. Identification of parameter settings based on loss probability requirements 1. Specify fraction Z that must satisfy loss requirement at small time scale 2. Identify possible parameter settings by checking curves above Z Z 3. Use the cross section of this parameter set and the set of parameters identified from overall loss probability figure NOBEL München meeting, June 2005

10. Results – bandwidth waste waste = sum of all wastei , wastei = max(allocated_bandwidthi – loadi, 0), where i is the time interval index Loss = total # packets lost/ total #packets offered Noteworthy result: increased packet loss and increased bandwidth waste come together! NOBEL München meeting, June 2005

11. Packet loss and bandwidth waste – dynamic behaviour NOBEL München meeting, June 2005

12. For comparison – fixed output rate (alpha = 1) This algorithm gives the intuitive result: packet loss decreases when bandwidth waste increases We conclude (preliminary) that the favourable property that loss and waste can be minimized simultaneously is an attribute of the dynamic algorithm NOBEL München meeting, June 2005

13. Conclusions • The dynamic algorithm makes it possible to minimize both overall packet loss and bandwidth waste simultaneously, very favourable • Lowest loss and lowest waste is obtained when when using • small time scale for resource allocation (Tres alloc= 0.1 sec) • A setting of algorithm parameter alpha that ensures measured loads have maximal impact on allocated resources (alpha = 0) • If, for some reason, low Tres alloc is not among the choices for practical implementation, the observed loss rates and bandwidth waste will be higher than those achievable for low Tres alloc • Additional comment: From a cost perspective it may not be favourable to do resource allocation very frequently. Hence the value T=0.1 may not be a real option. NOBEL München meeting, June 2005

14. Further work • Impact of period for the long time scale traffic variations • Impact of the amplitude of the traffic variations • More bursty small time scale traffic variations • Real traffic traces • Impact of buffer size on preliminary conclusions • Other resource allocation algorithms NOBEL München meeting, June 2005