1. Michael Markovitch Joint work with Gabriel Scalosub Department of Communications Systems Engineering Ben-Gurion University Bounded Delay Scheduling with Packet Dependencies

2. Real Time Video Streaming 2 Sandvine, “Global Internet phenomena report – 1H 2013”

3. Real Time Video Streaming Video streams are comprised of frames – Bursty traffic Video frames can be large (>>1500B) – Fragmentation Interdependency between different packets – Dropping some packets -> drop frame Packets MUST arrive in a timely manner 3

4. Current situation & Related work Best practices: – DiffServ AF queue for video streams – Admission control (average throughput) Number of streams can be large – Average throughput < channel access rate – Overlapping bursts >> momentary channel rate Related work – FIFO queuing with dependencies – Deadline scheduling without dependencies [MPR, 2011] [MPR, 2012] [EHMPRR, 2012] [KPS, 2013] [SML, 2013] [EW, 2012] [AMS, 2002] 4

5. Deadline scheduling Every packet has a deadline Focus on scheduling Queue size assumed unbounded More information (than FIFO) 5

6. Buffer and Traffic Model Single non-FIFO queue of infinite size (one hop) Discrete time: Every packet : – One of multiple packets in a frame – Has arrival time, deadline, size and value Goal: Maximize value of completed frames Arrival substep Delivery substep Cleanup substep Packets arriveOne packet deliveredPackets may be dropped 6

7. Buffer and Traffic Model 7 k = 12

8. Buffer and Traffic Model Uniform slack – d Packets can be scheduled on arrival 8 Arrival sequence schedule t t Arrival(p) Deadline(p) d d

9. Buffer and Traffic Model Finite burst size – b 9 Arrival sequence t b

10. Buffer and Traffic Model 10

11. Competitive analysis 11

12. A proactive greedy algorithm Ensures completion of at least one frame – Holds packets of only one frame 12 Arrival substep Delivery substep Cleanup substep Packets arriveOne packet deliveredPackets may be dropped

13. Proactive greedy - example Arrival sequence Proactive greedy schedule 13

14. Proactive greedy – competitiveness Competitive ratio –  Details in the paper Not far off from the lower bound 14

15. A better greedy algorithm 15 Why?

16. Greedy algorithm - analysis Competitive ratio –  Details in the paper We have a matching lower bound Reminder:  For proactive greedy – 16

17. What about the deadlines? Deadlines not used explicitly Bad news? – Worst case performance matches lower bound Good news – There is space for more interesting algorithms – Improve general performance How can deadlines be utilized? – Several approaches presented in the paper 17

18. Simulation Three online algorithms: – “Vanilla” greedy algorithm – Greedy algorithm with slack tie breaker – Opportunistic algorithm And the best current offline approximation 18

19. Simulation Simulation details: – Average throughput = channel access rate – 50 streams at 30FPS – Each stream starts at a random time Between 0 and 33ms – Random (short) time between successive packets “jitter” between packets of a single frame 19

20. Simulation results 20

21. To sum up First work considering both deadline scheduling and packet dependencies Very simplified model – Yet hard Improvements to the model – Non uniform slack – Randomization – Redundancy 21

22. Questions? markomic@post.bgu.ac.il 22

23. Video Streaming There are two main approaches for streaming video: – Large buffer – Small buffer Real time video streaming – small buffer – Can not send data before it is created 23

24. Example - Telesurgery 24 M. Anvari, C. McKinley, and H. Stein. Establishment of the World's First Telerobotic Remote Surgical Service, Ann Surg. Mar 2005; 241(3): 460–464.

25. Real time video streaming A single frame can require many IP packets (>>1500B) Real time video blues: – Dropping a single packet can result in an entire frame being dropped (no EC) – Too much delay → frame unusable – Best practice does not provide any guarantees 25

26. Proactive greedy - analysis Arrival sequence Proactive greedy schedule Direct mapping Indirect mapping 26

27. Tie breaking with deadlines The real world performance can be greatly affected by the choice of a tie breaking rule: – EDF (Earliest Deadline First) tie breaking This is a refinement of the greedy algorithm – Allows to keep as much frames alive as possible – Retains the scheduling preference 27

28. Non obstructive EDF Can we further refine the greedy algorithm? – How to keep even more frame alive? – Alter the scheduling preference so we can schedule a packet belonging to a frame of lower preference if according to current information a it will not cause a higher preference frame to expire An attempt to imitate the offline approximation by keeping as many frames alive as possible 28

29. Opportunistic provisional schedule 29 Slack 1 2 345

30. Lower bound for deterministic algorithms We assume that the maximal burst size b is finite, and that b≥2d We use competitive analysis to bound the performance of deterministic online algorithms For general traffic with bounded burst size, any deterministic online algorithm has a competitive ratio 30

31. Lower bound for deterministic (algorithms (cont Arrival sequence Online algorithm’s schedule Adversary’s schedule d=1, b=4 31

32. Proactive greedy - analysis Arrival sequence Proactive greedy schedule Direct mapping Indirect mapping 32

33. Proactive greedy – competitiveness 33

34. Greedy algorithm - analysis 34