Multimedia III Multimedia Systems Issues

1. Multimedia IIIMultimedia Systems Issues CS 423, Fall 2007 Klara Nahrstedt/Sam King

2. Multimedia OS vs. Conventional OS • Real time constraints! • Process scheduling • Rate Monotonic Scheduling • Earliest Deadline First • File System • Data must be supplied immediately • VCR operations • Multiple Users • File Placement • Caching • Disk Scheduling • Predictable • Static vs. Dynamic

3. Scheduling Environments • Traditional scheduling (time-sharing computers) • optimal throughput • optimal resource utilization • fairness • Traditional real time scheduling • guaranteed hard deadlines with no adaptation to changes of the workload • operations completed after deadline are useless • Multimedia scheduling • guaranteed soft deadlines, i.e., work in a dynamic environment with adaptation to changes of the workload in bounds of timing requirements • operations completed after deadline result in decreased quality

4. Multimedia Process Scheduling • Simplest case: files are fixed size, fixed resolution, fixed number of them. • Round robin is sufficient • Realistic case: • variable size • variable resolution (thus variable cpu time) • variable number

5. Real Time Processing • Tasks • Schedulable unit of the system • Timing Constraints • Periodic • Deadlines • Resource Requirements • CPU Time • Assumed independent • Preemptive or Non-preemptive • Task Manger • Admission Control • Determines a schedule • Must provide guarantees

6. S Ci Pi m < 1 U = i Schedulability • Scheduling algorithm guarantees a newly arrived task a deadline if the algorithm can find a feasible schedule. • Process utilization: • m: number of process • Ci: CPU time for process i • Pi: period of process i

7. Rate-Monotonic Scheduling • Process Requirements: • Must complete within period • Independent • Constant amount of CPU time per burst • If non-periodic then must not have deadlines • Preemptions are instant • Process priority is determined by the task rate • Example: process runs every 40 ms => 1000/40 = 25Hz => priority of 25 • Higher frequency equals higher priority • Higher priority process can preempt lower priority process

8. Rate-Monotonic Scheduling

9. Deadline-Based Scheduling • Process priority is determined by the process deadline. • Earliest Deadline First

10. Real Time Scheduling • Schedulability: • U = 10/30 + 15/40 + 5/50 = 0.808 • ~ 80% of the CPU is utilized • Schedulable!

11. Real Time Scheduling

12. Real Time Scheduling • Schedulability: • U = 15/30 + 15/40 + 5/50 = 0.975 • ~ 98% of the CPU is utilized • Schedulable!

13. Real Time Scheduling

14. S S Ci Pi Ci Pi m m < m(21/m-1) < 1 U = U = i i Admission Control • RMS • Liu and Layland proved in 1973 that a schedule could be guaranteed if: • m=3, U<0.780 • EDF • 100% utilization can be achieved :

15. Exam Problem • Consider three pre-emptible tasks: • Task A with PA = 200 ms, and CA = 60 ms • Task B with PB = 100 ms, CB = 40ms • Task C with PC = 1000 ms, CC = 10ms • Are these tasks schedulable at all? If yes, why, with which scheduling algorithm, and what is the schedule of tasks? If not, why not?

16. Exam Problem • Schedulability: • U = 60/200 + 40/100 + 10/1000 = 0.710 • < 0.780 so schedulable for RMS • Deadlines not provided so not schedulable for EDF

17. Multimedia File Systems • High data transfer rates • Large storage requirments • Data must be provided without delay • multimedia file server (video on demand) • difficult when requests are unplanned • Replace read system call with start • server then sends data at required rate • leave it to the user to handle the frame rate • user issues stop when finished • user can issue pause • rewind is easy, server simply starts over

18. Fast Forward and Rewind • Play back media at a higher rate than recorded (this is impractical) • Uncompressed data: • simply show every kth frame • Compressed data: • if frames are independent just need index of frames to find every kth • MPEG: I, P, and B frames • server uncompresses and recompresses every kth as I-frame • increase the amount of data transferred over the network • Plan ahead: create MPEG file with every 10th frame • audio and video are ussually separate!

19. Multiple Users • k users viewing the same movee = k users viewing k different movies • Near Video on Demand: • force user to watch at certain times • every 5 mins => 24 streams for 2 hour movie • No VCR functions • bus vs. taxi analogy

20. Adding VCR functions • Client buffers previous DT minutes and upcoming DT minutes • entails reading 2 streams at once • Data rate of 4Mbps, 10 minute buffer = 300 MB • If user fast forwards past buffer server can activate a temporary private stream • when user stops, refill buffer from next nearest stream • when caught up: • play from buffer • drop private stream • continue refilling buffer as before

21. File Placement • Multimedia files: • large • written once • read many times • read sequentially • strict quality of service criteria! • avoid seeking in middle of a frame • Single disk: • contiguous • small block model • large block model

22. Contiguous File Placement • Simple and avoids seeks within frames • Reasonable if planned ahead and layout doesn’t change • Problem if video, audio, and text in separate files • Interleave data: • No seeks on a single user system • Wastes resources reading unwanted data • Random access, fast forward/backward are impossible

23. Small / Large Block Models • Non-contiguous storage • Small Disk Block – disk block smaller than average frame size • Use Frame Index (Frame consists of all audio, video/text tracks for that frame as a contiguous run of disk blocks) • Called Constant Time Length • Large Disk Block – put multiple frames in each block • Use Block Index • Possibly store start frame • Called Constant Data Length • What are the tradeoffs? Disk blocks are larger than frames Disk blocks are smaller than average frame size

24. Large Block Model • Pros: • Index contains fewer entries • 256KB blocks, 16 KB frame, ~16 frames per block => 216,000 frames needs 13,500 entries • Runs disk at full speed • Cons: • Full N frames likely won’t fit in block • Leave unused: internal fragmentation, wasted space • Break frame: force seek within some frames • Double buffering hard • Fast forwarding requires massive I/O • Can use separate 10x file

25. Small Block Model • Pros: • Little disk waste • Double buffering easy (current/next frames) • Fast forward by displaying I-frames only • Cons: • Larger index • Page • Must store hole sizes (can’t use free list/bitmap) • Store lists for vaious size holes (per cylinder group)

26. Multiple Files on a Single Disk • Near Video on Demand • 5 min interval, 2 hour movie = 24 streams • store the 24 frames together • Some movies are more popular than others, hence we should take popularity into account • Zipf’s Law • Probability that the next customer will choose the item ranked ‘k-th’ in the popularity list is C/k, where C is normalization constant • C/1 + C/2 + …. + C/N = 1 9/22/2014 26

27. Organ-Pipe Algorithm • Place more popular files near center of disk • N=1000, P(top 5) = 0.307 • Head will be over these cylinders ~30% of the time

28. Caching • Multimedia Files ussually read from beginning to end • LRU doesn’t fit • Block Caching • File Caching

29. Block Caching • Multiple readers start to watch movie close to each other. • Store read block until all users have viewed it • Time between viewers determines size of cache needed • Merge streams • spead one up while slowing another down • use commercials

30. File Caching • In case files are very large (most movies are over 2GB), video servers cannot store movies on the disks • Servers keep the movies on DVDs or tapes • Cache/bring to the disk movie only if requested – means long start-up latency • Cache few first minutes of each movie on disk

31. Multimedia Disk Scheduling • Unlike regular files, these are very predictable • Static • Dynamic

32. Static Disk Scheduling Assumes all movies have the same frame rate Break stream frame requests into rounds sort blocks seek in order double buffer rounds for efficiency 9/22/2014 32

33. Dynamic Disk Scheduling • Movies have different frame rates • Requests appear more or less random • Requests have deadline (to maintain frame rate) • subract off service time • Scan-EDF • EDF: Serve requests whose deadlines are first • SCAN: Serve requests along the head movement • Collect requests whose deadlines are relatively close together and process these in cylinder order.

34. Conclusions • Multimedia systems put enormous real-time and data rate demands on all parts of operating system • However, multimedia systems have one property that makes it easier to handle them and that is predictability in many cases which can be utilized

35. Outline • Multimedia Process Scheduling • Soft-Real-Time • Rate Monotonic Scheduling • Deadline-based Scheduling • Multimedia Storage • Placement • Caching • Disk Scheduling

36. Real-Time Processing Requirements • Occurs in predetermined - usually periodic – intervals • Complete at certain deadlines. • Real time process manager • performs admission control • determines a schedule • gives processing guarantees. • Goal of Multimedia Process Scheduling • non-RT process should not starve when RT process is running • RT process should not be subject to priority inversion. • Problem: how to find a feasible schedule.

37. System Model in RT Scheduling • Task is the schedulable unit of the system. • Task is characterized by (1) timing constraints and resource requirements.

38. Assumptions and Notation • Assumptions : periodic tasks are mutually independent. • Time Constraints : (s, e, d, p) where s is starting point, e is processing time, d is deadline, and p is period. • If the period at (k-1) step is equal to ready(start) time of period k, then we get congestion avoidance deadline. • Tasks : can be preemptive or non-preemptive. • Multimedia Scheduling Algorithm : must determine sharing of the resource used by different tasks so that all of them can meet their deadlines.

39. Schedulability • Scheduling algorithm guarantees a newly arrived task a deadline if the algorithm can find a feasible schedule. • Guarantee ratio := number of guaranteed tasks/total number of tasks • Another performance metric is process utilization with .

40. Admission Control Process utilization for RM algorithm is upper bounded by (Liu and Layland condition). The schedulability test is then With EDF algorithm, the 100% utilization can be achieved : The schedulability test is then 9/22/2014 40

41. RMS vs EDF • In terms of context switching, the EDF is better if more than one stream are processed concurrently

42. Multimedia Server • Multimedia file servers act like VCRs • Video-on-Demand systems • Near Video-on-Demand systems • Multimedia file servers with streaming model are often called push servers • Traditional file servers are called pull servers

43. Storage Requirements • Real-time Storage and Retrieval • High Data Transfer Rate and Large Storage Space • HDTV quality requires (1280x720 pixels/frame with 24 bits/pixel) 81 Mbytes per second • NTSC quality requires (640x480 pixels/frame with 24 bits/pixel) 27MBytes per second

44. Continuous Media Recording and Playback Media Quanta Quanta read from server disk Quanta Buffered Quanta consumed Time Challenge of multimedia server is to ensure continuous retrieval of a media Stream from the disk.

45. Support of Continuous Media • Our goal is to provide a constant and timely retrieval of data. • Following approaches are taken • Proper management of multimedia disk storage; result is an optimized organization of files on disk • Admission control for different service classes • Special disk scheduling algorithms and sufficient buffers to avoid jitter