Fuzzy RED: Congestion control for TCP/IP Diff-Serv

1. Fuzzy RED: Congestion control for TCP/IP Diff-Serv • Explosion of Internet – new congestion control method is needed • WHY? : • Users now demand for integrated services network • New services with high bandwidth demands (video on demand, video conferencing, etc) • Need for quality of service demanded by new applications (e.g. streaming video) • Current internet infrastructure can only support best effort traffic

2. Diff-Serv • A more evolutionary approach than Int-Serv • Int-Serv had connection establish overheads. • In Internet based applications they were in some cases bigger than the existing connection period • Diff-Serv does not require significant changes to the Internet Infrastructure • Uses existing ToS bits in IP header of service differentiation • Works in the edges of a network • Provides QoS using drop-preference algorithm

3. Diff-Serv • Differentiation of services is provided through 3 classes of services called per-hop behaviour • Expedited Forwarding – EF: • Low losses • Very low queuing delays • Allocation of resources through SLA at connection setup

4. Diff-Serv • Assured Forwarding – AF: • Low packet losses • Has 3-4 independent forward classes • Each such class has 2-3 different drop preferences • Preferentially drops best-effort packets and non-conforming packets when congestion occurs

5. RED Most popular algorithm used for Diff-Serv networks: for each packet arrival calculate the average queue size avg if minth avg maxthcalculate probability pawith probability pa: mark the arriving packet else if maxth avg mark the arriving packet Explain what mark means

6. RED • Uses min, max dropping thresholds for each class • The algorithm used for calculating the queue average determines the allowed degree of burstiness • The pa probability is a function of the average queue size. Varies linearly from 0 to 1.

7. EF AF Class Best Effort Yes Priority Queue Check and traffic shaping No Discard RIO Queue Max Min RED

8. Fuzzy RED • We replaced fixed thresholds with an FLC • FLC - Fuzzy Logic Controller: • Calculates pa based on two inputs, queue size and queue rate of change • The two inputs are described by fuzzy sets • The FLC determines the pa by applying a set of rules. • Each class of service has an FLC

9. Fuzzy RED - The algorithm Algorithm: for each packet arrival calculate queue size, queue rate of change calculate probability pa based on above metrics with probability pa mark the arriving packet pa is calculated by the FLC

10. Fuzzy RED – Input Sets • FLC set for queue - q (90 packet buffer) empty 0 0 18 35 moderate 20 33 42 63 full 44 64 90 90 • FLC set for queue rate of change - dq decreasing -44 -44 -7 –1 zero -14 0 0 14 increasing 1 7 44 44

11. Fuzzy RED – Output Set • FLC set for pa zero –20 –20 0 0 low 0 0.10 0.10 0.20 medium 0.15 0.20 0.20 0.30 high 0.20 0.60 0.60 1.0 • As with input sets pa can also be different for each class of service • Best describe the behavior expected by the network administrator

12. Fuzzy RED - Rules • Based on linguistic rules we calculate the dropping probability • Each class of service has its own definition of set and rules • Sample of rules: • If q is empty the pa is zero • If q is full and dq is zero the pa is medium • If q is full and dq is increasing then pa is high

13. Fuzzy RED – Calculating pa Input Evaluation Output Evaluation Rule: If q is full and dq is decreasing then pa is high 1 0.4 44 64 90 -20 -7 –1 0.2 0.6 1 Input: q is 68 and dq is -3 Output: the weighted average of the colored surface: 0.4

14. Fuzzy RED - Conclusions Advantages: • Simplicity - just 3 steps • Effectiveness • Scalability - each class has its own FLC rule file • Robustness • Currently ………