1. Service Differentiation in Optical Burst Switched Networks Chi-Hong Loi, Wanjiun Liao, and De-Nian Yang GLOBECOM’02

2. Proportional QoS The loss rate of each class is maintained in a predefined proportion according to the priority. An arrival packet will be dropped if its predefined loss rate is violated regardless of whether there is an idle channel. This approach always causes excessive dropping.

3. Protocol Fundamentals There are k different classes c 1, c 2, …,c k with ascending priority order. Let p i be the usage limit assigned to class c i. 0 ≤ p 1 < p 2 < … < p k ≤ 1, and. Usage profile: Usage limit. Current usage. List of scheduled requests in the same class (burst duration outgoing wavelength and a predefined timer).

4. In-Profile Verification A class is said to be in profile if its current usage does not exceed a predefined usage limit. The current usage of class c i is defined as where n j is the number of scheduled requests of class c j in (t, t+τ), and l j is the burst length of request R j. Class ci is in profile if ρ i ≤ p i ; otherwise, the class is out of profile.

5. Burst Preemption Suppose that a switch has newly received a in-profile class request R x, with start time s x and end time e x. Let C o be a set of candidate classes to be preempted, defined as C o = {c i | ρ i > p i, i = 1, 2, …k}

6. Operation Overview New Burst Eligible? Yes Scheduled In-profile? No Find a previous request from out-of-profile class and make a preemption. Update profile No Dropped Yes

7. Analytical Results Let denotes the state of the Markov chain, where n i is the number of wavelengths used by class c i.

8. Analytical Results (continued)

9. Simulation Results Blocking probabilities of the three mechanisms.

10. Simulation Results (continued) Blocking probabilities of the three mechanisms with network congestion.

11. Simulation Results (continued) System utilization.

12. Conclusion A new bufferless mechanism was described using a preemptive wavelength reservation mechanism to differentiate services in OBS networks. The approach performs best in terms of lower blocking probability and higher resource utilization.