1 Proportional differentiations provisioning Packet Scheduling & Buffer Management Yang Chen LANDER CSE Department SUNY at Buffalo

2 Outlines Motivations and terms Proportional differentiation Implementations and related issues Conclusion and Future works

3 Quality of Service (QoS) What is QoS? A measurement of how well the network behaves and an attempt to define the characteristic and properties of specific services. Who need QoS? User:  More applications have strict service requirements: low packet loss rate, short delay, etc; Network operator:  Resource in a network must be used efficiently;

4 Intserv Integrated Service Try to achieve per-flow, end to end service guarantees; Per-flow state is kept at intermediate router; Admission control, resource reservation and corresponding signaling are required;

5 Diffserv Differentiated Service Aggregate individual flows with similar QoS requirements; No complex signaling; Can be implemented gradually (on the congested links);

6 Differentiated Service Absolute (quantitative) Provide a macro-flow with a quantitative performance level. Relative (qualitative) Provide a number of classes with increasing performance.

7 Primary Tradeoffs Fairness Access to excess capacity Isolation Protection from excess traffic from other users Efficiency Number of flows that can be accommodated for a given level of service Complexity In terms of implementation and control overhead

8 QoS metrics of interest in packet networks Average packet delay Packet loss rate Deadline violation probability Jitter Etc….

9 Scheduling and buffer management Scheduling Support service differentiation on bandwidth by controlling the actual transmission of packet. Take effect on time-related QoS metrics. Buffer management Support service differentiation on buffer by deciding which packet can be stored for future transmission. Take effect on loss-related QoS metrics.

10 Outlines Motivations and terms Proportional differentiation Implementations and related issues Conclusion and Future works

11 Proportional Differentiation Definition If q i is the QoS metric of interest, and s i is the differentiation factor for class i, we have: For example: Given two classes 1 and 2, and the QoS metric is packet loss rate, s 1 =1; s 2 =2, the packet loss rate of class 2 should be twice that of the loss rate of class 1.

12 Proportional Differentiation Pros Controllable Differentiation level between service classes can be controlled by network operator; Predictable Performance of higher classes is consistently better than the performance of lower Class even in short time scale;

13 Outlines Motivations and terms Proportional differentiation Implementations and related issues Conclusion and Future works

14 Recall: QoS metrics of interest Average packet delay Packet loss rate Deadline violation probability Jitter Etc….

15 Proportionally differentiated packet delay Waiting Time Priority (WTP) Scheduling One packet need to be scheduled On-line priority measurement is done Class 0 Class 1 Class N

16 Class 0 Class 1 Class N Class 1 has the highest priority Proportionally differentiated packet delay Waiting Time Priority (WTP) Scheduling

17 Proportionally differentiated packet delay Wait Time Priority (WTP) Scheduling Suppose class i is backlogged at time t, and that w i (t) is the head waiting time of class i at t; We have normalized head waiting time of class i at t as: When a packet need to be scheduled, a backlogged class j is selected for

18 Proportionally differentiated packet delay Proportional Average Delay scheduling Hybrid Proportional Delay scheduling Backlog Proportional Rate scheduling Etc….

19 Proportionally differentiated loss rate Buffer Management Class 0 Class 1 Class 2 Total buffer size 20 One packet arrives On-line priority measurement is done

20 Proportionally differentiated loss rate Buffer Management Class 0 Class 1 Class 2 Total buffer size 20 Class 0 has the lowest priority

21 Proportionally differentiated loss rate Buffer Management Class 0 Class 1 Class 2 Total buffer size 20

22 Proportionally differentiated loss rate Proportional Loss Rate (PLR) dropper Suppose there are two counters for each class i, counter a i records packet arrival history of class i, counter d i records packet drop history of class i; We have normalized packet loss rate of class i as: When a packet needs to be dropped, a backlogged class j is selected for

23 Proportionally differentiated loss rate PLR(  ) Using the entire packet loss history PLR(M) Using the most recent M packet entry PLR with active resetting Using the most recent packet entry with variable history length within a limited deviation on proportional relations Predicting the average drop distance d i is the average number of successfully forwarded packets between two packet drops, loss rate l i is 1/d i ;

24 Loss rate and Packet delay Fluid flow assumption Service rate of class i is r i ; Loss rate of class i is l i ; An optimization problem is formulated with Objectives:  Minimum service rate changes  r i ;  Minimum loss rate l i ; Constraints:  Proportional relations on loss rates and packet delays of different service class;

25 Deadline violation probability Motivation Performance of multimedia applications do not depend on average delay much but on the probability that the transmission delay exceeds a certain threshold Deadline Each class i is associated with a delay bound  i. A packet of class i arriving at time t A will receive a tag t A +  i as its deadline.

26 Deadline violation probability Earliest Deadline First (EDF)/Earliest Deadline Due scheduler Shortest Time to Extinction (STE) scheduler Cons: Only provide different deadline for each service class, no differentiation for deadline violation probability, which is an important factor on some real-time application’s performance, e.g., Voice over IP.

27 Deadline violation probability Weighted EDF/EDD Provides differentiated deadline violation probability. If the scheduler is in “congested mode”, WEDF scheduler is applied Class 0 Class 1 Class N

28 Deadline violation probability “Congested Mode” There are more than one backlogged class with the first packet with a deadline t A +  i <t s +  i (t s is the system time,  i is a safety margin, e.g.,  i =  i /10). WEDF scheduler In “congested Mode”, a class j with largest normalized measurement-based deadline violation probability is served.

29 Proportionally differentiated Jitter Jitter Jitter of one packet is the difference of this packet’s queueing delay and the delay of preceding packet. Motivation Jitter will affect the performance of both interactive and non-interactive applications involving digital continuous media.

30 Proportionally differentiated Jitter The long time average jitter for served packets of each class is recorded as j i * (t); The minimum jitter for all the packets in the queue is calculated as j i min (t) The average jitter for class i is: Where: n i (t): the packet of class i been served; q i (t): the packet of class i in the queue.

31 Proportionally differentiated Jitter Normalized average jitter When a packet need to be scheduled, a backlogged class j is selected for

32 Problems in the implementation Problems Delay/Jitter differentiation  Difficult to provide accurate proportional differentiation on both long time and short time periods;  Hybrid solution will introduce extra computation; Loss rate/violation probability  Keeping the entire loss/violation history will give accurate only on long term average;  Keeping the most recent history will help the system to achieve accurate differentiation on short time period but requires extra hardware and operation.

33 Feasibility Problem in this QoS model Feasible A set of proportional factors is feasible when there exists a work-conserving scheduler that can set the differentiation level as this set specifies. Feasibility depends on traffic profile: total load and percentage of each class.

34 Feasibility Problem in this QoS model Analysis on average delay Conservation Law Assume all classes have the same packet size distribution as 1.

35 Feasibility Problem in this QoS model Analysis on average delay (cont.) There is a lower bound for delay of each class. This lower bound would result if that class was given strict priority over the rest of the traffic Given a steady traffic profile, one method has been proposed to figure out the feasible region of proportional factors

36 Feasibility Problem in this QoS model Assume all classes have the same packet size distribution. The necessary and sufficient feasibility conditions are N-1 inequalities Where are the average delay for service classes from k to N, which are given the strict priority over all other Service classes. All the values of can be achieved either experimentally or theoretically.

37 Feasibility Problem in this QoS model Assume there are two service classes:

38 Outlines Motivations and terms Proportional differentiation Implementations and related issues Conclusion and Future works

39 Conclusion Proportional differentiation is versatile. This QoS model can be implemented on various QoS metrics; Proportional differentiation is controllable. The level of differentiation can be adjusted by setting different proportional factors; Proportional differentiation is predictable. It can keep the proportional relations even in short time period;

40 Conclusion However In order to provide finer differentiation, as a tradeoff, complexity increases in terms of implementation and control overhead. Infeasibility situation exists on some traffic profiles with no efficient solution.

41 Future works Feasibility testing How to judge whether the proportional factors are properly in a dynamic traffic condition? Class selection How to selection a service class for a particular traffic flow in order to fulfill end-to-end/absolute QoS requirements? Class provisioning Given traffic conditions and proportional factors, how much resource shall we provide?

42 Main References C. Dovrolis and D. Stiliadis and P. Ramanathan “Proportional Differentiated Services: Delay Differentiation and Packet Scheduling.” C. Dovrolis and P. Ramanathan “Proportional Differentiated Services, Part II: Loss Rate Differentiation and Packet Dropping.” J. Liebeherr and N. Christin “Buffer Management and Scheduling for Enhanced Differentiated Service” S. Bodamer “A New Scheduling Mechanism to Provide Relative Differentiation for Real-Time IP Traffic.” T. Quynh, et al. “ Relative Jitter Packet Scheduling for Differentiated Services”

43 Q&A