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Proportional differentiations provisioning Packet Scheduling & Buffer Management

Proportional differentiations provisioning Packet Scheduling & Buffer Management

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Proportional differentiations provisioning Packet Scheduling & Buffer Management

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  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 qi is the QoS metric of interest, and si 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, s1=1; s2=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 Class 0 Class 1 Class N On-line priority measurement is done

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

  17. Proportionally differentiated packet delay • WaitTime Priority (WTP) Scheduling • Suppose class i is backlogged at time t, and that wi(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 On-line priority measurement is done Class 0 Class 1 One packet arrives Class 2 Total buffer size 20

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

  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 ai records packet arrival history of class i, counter di 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 • di is the average number of successfully forwarded packets between two packet drops, loss rate li is 1/di;

  24. Loss rate and Packet delay • Fluid flow assumption • Service rate of class i is ri; • Loss rate of class i is li; • An optimization problem is formulated with • Objectives: • Minimum service rate changesri; • Minimum loss rate li; • 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 tA will receive a tag tA+ 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 tA+i<ts+i (ts 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 ji*(t); • The minimum jitter for all the packets in the queue is calculated as jimin(t) • The average jitter for class i is: Where: ni(t): the packet of class i been served; qi(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