Offset-Time-Based QoS Scheme

1. Offset-Time-Based QoS Scheme • Extend JET to achieve isolation of traffic classes • The burst blocking probability of a priority class is independent of the offered load from the lower-priority classes • Basic idea: give a larger extra offset time to a higher priority class • Reservation for a higher priority burst has a better chance to succeed. • Burst assembly: multiple IP packets are assembled into a burst based on their destination address and priority class

2. Offset-Time-Based QoS Scheme • Assume two classes of services: class 0 and class 1 (class 1 has higher priority) • An extra offset time is given to class 1 traffic, but not to class 0 traffic • Assume the base offset time is negligible compared to the extra offset time • Assume a link has only one wavelength for data and an additional wavelength for control Notations: • req(i) : class i request, i = 0,1 • : the arriving time for req(i) • : the service start time for req(i) • : the burst length requested by req(i)

3. Offset-Time-Based QoS Scheme • A class 1 request can obtain a higher priority for wavelength reservation than a class 0 request • Case 1: req(1) comes before req(0) • Req(1) will succeed • Req(0) will be blocked if and , or

4. Offset-Time-Based QoS Scheme • Case 2: req(0) comes before req(1) • When , req(1) would be blocked if • The blocking can be avoided by using a large enough offset time so that • needs to be larger than the maximum burst length in class 0

5. Offset-Time-Based QoS Scheme

6. Offset-Time-Based QoS Scheme • When extra offset time for req(1) is large enough: • The blocking probability of class 1 is only a function of the offered load belonging to class 1 • The blocking probability of class 0 depends on the offered load belonging to both classes • Extend to n (n>2) classes • An extra offset time is given to class i traffic (0 < i  n) • Assume burst duration has exponential distribution, when , the probability that req(i) will not be blocked by req(i-1) is 95% (L = average duration of a burst in class i-1)

7. Other QoS Schemes: Intentional Dropping • Problems of offset-time-based QoS scheme • The extra offset time introduces an additional delay at the edge • Performance of differentiation depends on the burst length • Unfair to long bursts belonging to low priority classes • Intentional dropping [Chen et al, Globecom 01] • Address the problems of offset-time-based QoS scheme • Bursts are selectively dropped according to loss rate measurement to achieve proportional burst loss probability

8. Intentional Dropping • Proportional QoS model: want • lossratei : the burst loss rate of class i • si : proportional factor associated with class i, set by service provider • Notations • lossi: bursts dropped of class i • arrivali : bursts arrival of class i • ERROR :a parameter that controls the accuracy of proportional relations • lossratei= lossi/ arrivali:online blocking probability measurement for class i;

9. The Algorithm

10. The Algorithm Resetting counters from time to time ensures that the online measurement is done over the most recent traffic history

11. Other QoS Schemes: Burst Segmentation • Achieve differentiation at the packet level • Packets from low priority service classes are assembled to form the tail or head of each burst • Packets from high priority service classes are assembled in the middle of each burst. • Segment at the tail or head of a burst that overlaps with another burst are dropped when contention happens

12. Burst Scheduling Algorithms • Assume nodes have wavelength conversion capability • Job of burst scheduler • Choose a proper wavelength for the burst considering the existing reservations made on each wavelength • Make a new reservation on the selected channel

13. Horizon • Also called LAUC (latest available unscheduled channel) • For each wavelength, the scheduling horizon is maintained • Scheduling horizon: the latest time at which the wavelength is currently scheduled to be in use • The channels whose scheduling horizon precede the new burst’s arrival time are considered available • An available channel with the latest scheduling horizon is chosen • Minimize the gap created • Drawback: waste gaps between two existing reservations

14. LAUC with Void Filing (LAUC-VF) • Given a data burst with arrival time t and duration L • Find the outgoing channels that are available for the time period of (t, t+L). • If there is at least one such channel, select the latest available channel, i.e., the channel having the smallest gap between t and the end of last data burst before t • Minimize the void generated between the start of new reservation and an existing reservation

15. Variants of LAUC-VF • Min-EV (Ending Void): try to maximize the new void generated between the end of new reservation and an existing reservation • Best Fit: try to minimize the total length of starting and ending voids generated after the reservation • LAUC-VF and its variants have comparable bandwidth utilization that is much higher than Horizon

16. Illustration of Scheduling Algorithms

17. Contention Resolution • Contention occurs when burst scheduler can’t find an available channel for the new burst • Ways to resolve contention • Deflection • Dropping • Preemption • Burst segmentation

18. Deflection • A burst is sent to a different output channel instead of the preferred one • Deflection can be applied in wavelength, space, and time domains • Wavelength domain: the burst is sent on another wavelength through wavelength conversion • Space domain: the burst is sent to a different output port • Time domain: the burst is delayed for a fixed time by passing through an FDL

19. Dropping/Preemption/Burst Segmentation • If a burst can’t be deflected, it can be dropped • An incoming burst can preempt an existing burst • Burst segmentation: segment at the tail or head of a burst that overlaps with another burst is dropped or deflected