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Fair Queuing

Fair Queuing. Hamed Khanmirza Principles of Network University of Tehran. Fairness. Fairness Goals. Allocate resources fairly Isolate users Router does not send explicit feedback to source Still needs e2e congestion control Still achieve statistical muxing

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Fair Queuing

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  1. Fair Queuing Hamed Khanmirza Principles of Network University of Tehran

  2. Fairness

  3. Fairness Goals • Allocate resources fairly • Isolate users • Router does not send explicit feedback to source • Still needs e2e congestion control • Still achieve statistical muxing • One flow can fill entire pipe if no contenders • Work conserving  scheduler never idles link if it has a packet

  4. What is Fairness? • MAX-MIN Fairness • No user receives more than its share • No other scheme satisfy this condition which has higher min allocation Ui = MIN (Ui,Ri) - • What does “user” means? • Per source of packet • Restrict for example File Servers • Per receiver • Malicious user can reduce receiver bandwidth • Per host process • Encourage user to start several communication simultaneously • Source-Destination pair • Allows source consume unlimited resources for different destinations • However the last one seems the best trade-off

  5. Queuing Algorithm for Fairness

  6. Queuing Algorithms • Controls: • Order of sending packets • Buffer usage • Queuing Algorithms do not directly affect congestion, must be combined with intelligent queue manager • Why not simple round robin? • Variable packet length  can get more service by sending bigger packets • Unfair instantaneous service rate • What if arrive just before/after packet departs?

  7. Implementing MAX-MIN Fairness • Generalized processor sharing (GPS) • Fluid fairness • Bitwise round robin among all queues • We assume we have a server which can service N bits simultaneously from N flows. • Generally we have a variable keeps amount of service that each flow has received since last busy period • Objective is equalizing this amount named as “potential” • Approaches differs in way updating potentials

  8. Implementing MAX-MIN Fairness • One way is defining “System Potential (Virtual Time)” function • When a flow goes idle, after activation its potential is set equal to value of system potential • Different approaches have different functions to maintain System Potential

  9. GPS

  10. Packet by Packet GPS • In packet system GPS is not feasible • Though, scheduler can not update potentials of all flows at the same rate • One of the well known approaches is WFQ (Weighted Fair Queuing) • Single flow: clock ticks when a bit is transmitted. For packet i: • Pi = length, Ai = arrival time, Si = begin transmit time, Fi = finish transmit time • Fi = Si+Pi = max (Fi-1, Ai) + Pi • Multiple flows: • Send packet with smallest Fi

  11. WFQ (Weighted Fair Queuing) 1 1 Wq = 10 Si Fi 10 10 Wq = 1 10 10 Wq = 1

  12. Delay Allocation • Reduce delay for flows using less than fair share • Advance finish times for sources whose queues drain temporarily • Schedule based on Bi instead of Fi • Fi = Pi + max (Fi-1, Ai)  Bi = Pi + max (Fi-1, Ai - d) • If Ai < Fi-1, conversation is active and d has no effect • If Ai > Fi-1, conversation is inactive and d determines how much history to take into account • Infrequent senders do better when history is used

  13. PP GPS • Problems • Needs Sorting at least among active sessions • System Potential Problem • How we should store? With Time ? How about for variable bit rate servers? • When we must update System Potential? • How we must assign newly arrived packet potential during a packet is sending • System Potential is always increasing function of time • If busy time lasts too long, System Potential maybe overflow • Is It fair enough??

  14. WFQ & GPS • Suppose a system from start • Session 1 has 11 packets with weight 0.5 • 10 other sessions has weight 0.05 • All packets has 1 unit length • Our system services 1 packet at one time unit

  15. WFQ & GPS WFQ GPS

  16. WFQ & GPS • Impact on Link Sharing & Real Time Service • Much larger delay bounds • Impact on Feedback-Based Algorithms • Oscillation of RTT The Most Exact Approximation :WF2Q

  17. WF2Q • selects packet among Eligible ones no from all of the packets in all sessions (SEFF Policy). • Eligible means set of packets which has started receiving service in GPS system at time t. • We have one Start and Finish time for a session not for each packet

  18. WF2Q WF2Q GPS

  19. WF2Q WFQ WF2Q

  20. WF2Q & WF2Q+ • Yes, we achieved better granulity but • Now we have to sort twice ! • How we should compute Virtual Time ! • We should run GPS system in background ! • WF2Q+ : Has exactly the same properties with lighter complexity

  21. Other Famous Variations • Self Clocked FQ (SCFQ) • Calculating Virtual Time • V(t) is the finish time of the packet being serviced or was serviced • Start Time FQ (STFQ) • Calculating Virtual Time • During busy period, V(t) is the Si of the packet being serviced • At the end of busy period V(t) is Fi of the last packet was serviced • SSF (Smallest Start time First)

  22. Other Simple Approaches • FIFO • Strict Priority • Starvation • Just for important flow or determined rate flows • RR • Simply do round robin • Do not consider packet lengths • DRR (Deficit Round Robin) • Each Queue has a “quantum” based on its weight and a “Deficit Counter” • In each round can send quantum bytes and the Deficit counter keeps count of sent bytes and remained quantum • If the queue is empty Deficit counter will be set to zero

  23. Deficit Round Robin DC = 400 700 64 200 500 DC = 500 √ 100 100 100 100 DC = 700 1024 512 Quantum = 1500

  24. Deficit Round Robin DC = 400 700 64 200 500 DC = 100 DC = 700 √ 1024 512 Quantum = 1500

  25. Deficit Round Robin DC = 400 700 64 200 500 DC = 0 DC = 188 1024 Quantum = 1500

  26. Deficit Round Robin DC = 1900 √ 700 64 200 500 DC = 0 DC = 1688 1024 Quantum = 1500

  27. DRR++ 250 500 QLC = 1000 QBE = 1500 DC = 400 700 64 200 500 DC = 500 100 100 100 100 DC = 700 1024 512

  28. DRR++ • Advantages • Reduces delay comparing with DRR • Reduces burst comparing with PQ • BUT … • Sensitive to transient bursts • Hard to configure (QLC) • Esp. for TCP traffic in the core! • worst-case condition configuration

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