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 can be made in much advance than lower priority bursts

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)

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

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

Offset-Time-Based QoS Scheme

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)

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 Density function f(x) = lambda*e^(-lambda*x), mean = 1/lambda

Intentional Dropping Proportional QoS model: want Notations 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;

The Algorithm

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

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

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

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

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

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

Illustration of Scheduling Algorithms

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

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

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