1 / 77

On Managing Continuous Media Data

This research explores the challenges of managing continuous media data, focusing on optimizing cost, latency, and throughput. The study scrutinizes conventional wisdom regarding disk latency, throughput, and utilization, particularly in the context of large-scale media applications. Addressing the requirements for just-in-time data delivery to simultaneous users, it highlights innovative solutions such as the "BubbleUp" scheduling strategy to improve performance. By analyzing various disk scheduling policies and their impact on memory utilization, the work proposes effective strategies for efficient media data management.

kovit
Télécharger la présentation

On Managing Continuous Media Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. On Managing Continuous Media Data Edward Chang Hector Garcia-Molina Stanford University

  2. Challenges • Large Volume of Data • MPEG2 100 Minute Movie: 3-4 GBytes • Large Data Transfer Rate • MPEG2: 4 to 6 Mbps • HDTV: 19.2 Mbps • Just-in-Time Data Requirement • Simultaneous Users

  3. ...Challenges • Traditional Optimization Objectives: • Maximizing Throughput! • Maximizing Throughput!! • Maximizing Throughout!!! • How about Cost? • How about Initial Latency?

  4. Related Work • IBM T.J. Watson Labs. (P. Yu) • USC (S. Ghandeharizadeh) • UCLA (R. Muntz) • UBC (Raymond Ng) • Bell Labs. (B. Ozden) • etc.

  5. Outline • Server (Single Disk) • Revisiting Conventional Wisdom • Minimizing Cost • Minimizing Initial Latency • Server (Parallel Disks) • Balancing Workload • Minimizing Cost & Initial Latency • Client • Handling VBR • Supporting VCR-like Functions

  6. Conventional Wisdom(for Single Disk) • Reducing Disk Latency leads to Better Disk Utilization • Reducing Disk Latency leads to Higher Throughput • Increasing Disk Utilization leads to Improved Cost Effectiveness

  7. Is Conventional Wisdom Right? • Does Reducing Disk Latency lead to Better Disk Utilization? • Does Reducing Disk Latency lead to Higher Throughput? • Does Increasing Disk Utilization lead to Improved Cost Effectiveness?

  8. S DR Memory Use TR -- DR Tseek Tseek T Time Tseek: Disk Latency TR: Disk Transfer Rate DR: Display Rate S: Segment Size (Peak Memory Use per Request) T: Service Cycle Time

  9. S DR Memory Use TR -- DR Tseek Tseek T Time S = DR × T T = N × (Tseek + S/TR)

  10. Disk Utilization N × TR × DR × Tseek S = TR - N × DR S is directly proportional to Tseek S/TR Dutil = S/TR + Tseek Dutilis Constant!

  11. Is Conventional Wisdom Right? • Does Reducing Disk Latency lead to Better Disk Utilization? NO! • Does Reducing Disk Latency lead to Higher Throughput? • Does Increasing Disk Utilization lead to Improved Cost Effectiveness?

  12. What Affects Throughput? Disk Utilization × Disk Latency Throughput ? Memory Utilization

  13. Memory Requirement • We Examine Two Disk Scheduling Policies’ Memory Requirement • Sweep (Elevator Policy): Enjoys the Minimum Seek Overhead • Fixed-Stretch: Suffers from High Seek Overhead

  14. Per User Peak Memory Use N × TR × DR × Tseek S = TR - N × DR

  15. Sweep (Elevator) • Disk Latency: Minimum • IO Time Variability: Very High B1 A1 A2 B2

  16. Sweep (Elevator) • Memory Sharing: Poor • Total Memory Requirement: 2 * N * Ssweep

  17. Fixed-Stretch • Disk Latency: High (because ofStretch) • IO Variability: No (because ofFixed) a b a b a

  18. Fixed-Stretch • Memory Sharing: Good • Total Memory Requirement: 1/2 * N * Sfs

  19. Sweep 2 * N * Ssweep Available Memory = 40 Mbytes N = 40 Fixed Stretch 1/2 * N * Ssf Available Memory = 40 Mbytes N= 42 Higher Throughput Throughput * Based on A Realistic Case Study Using Seagate Disks

  20. What Affects Throughput? Disk Utilization × Disk Latency Throughput ? Memory Utilization

  21. Is Conventional Wisdom Right? • Does Reducing Disk Latency lead to Better Disk Utilization? NO! • Does Reducing Disk Latency lead to Higher Throughput? NO! • Does Increasing Disk Utilization lead to Improved Cost Effectiveness?

  22. Per Stream Cost Cost Per Stream Cost Memory Cost Disk Cost Number of Users

  23. Per-Stream Memory Cost Cm × N × TR × DR × Tseek Cm× S = TR - N × DR

  24. Example • Disk Cost: $200 a unit • Memory Cost: $5 each MBytes • Supporting N = 40 Requires 60MBytes Memory • $200 + 300 = $500 • Supporting N = 50 Requires 160 MBytes Memory • $200 + 800 = $1,000 • For the same cost $1,000, it’s better to buy 2 Disks and 120 Mbytes to support N = 80 Users! • Memory Use is Critical

  25. Is Conventional Wisdom Right? • Does Reducing Disk Latency lead to Better Disk Utilization? NO! • Does Reducing Disk Latency lead to Higher Throughput? NO! • Does Increasing Disk Utilization lead to Improved Cost Effectiveness? NO!

  26. So What?

  27. Outline • Server (Single Disk) • Revisiting Conventional Wisdom • Minimizing Cost • Minimizing Initial Latency • Server (Parallel Disks) • Balancing Workload • Minimizing Cost & Initial Latency • Client • Handling VBR • Supporting VCR-like Functions

  28. Initial Latency • What is it? • The time between when a request arrives at the server to the time when the data is available in the server’s main memory • Where is it important? • Interactive applications (e.g., video game) • Interactive features (e.g., fast-scan)

  29. Sweep (Elevator)

  30. Fixed-Stretch • Space Out IOs Playback Point S M e m o r y Transfer Seek Time a b C a b

  31. Fixed-Stretch a S1 b c S3 S2

  32. Fixed-Stretch S1 S3 S2

  33. Our Contribution: BubbleUp • Fixed-Stretch Enjoys Fine Throughput • BubbleUp Remedies Fixed-Stretch to Minimize Initial Latency

  34. Schedule Office Work • 8am: Host a Visitor • 9am: Do Email • 10am: Write Paper • 11am: Write Paper • Noon: Lunch

  35. BubbleUp S1 S3 S2

  36. BubbleUp • Empty Slots are Always Next in Time • No additional Memory Required • Fill the Buffer up to the Segment Size • No additional Disk Bandwidth Required • The Disk Is Idle Otherwise

  37. Evaluation 9 7 5 Latency (S) Sweep 3 1 BubbleUp N

  38. Fast-Scan S1 S2 S3

  39. Fast-Scan S1 S2 S4 S3

  40. Data Placement Policies • Please refer to our publications

  41. S1 S2 S3

  42. Chunk Allocation • Allocate Memory in Chunks • A Chunk = k * S • Replicate the Last Segment of a Chunk in the Beginning of Next Chunk • Example • Chunk 1: s1, s2, s3, s4, s5 • Chunk 2: s5, s6, s7, s8, s9

  43. Chunk Allocation • Largest-Fit First • Best Fit (Last Chunk)

  44. 18 Segment Placement 4 16 8

  45. Largest-Fit First 4 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 v16 8

  46. Best Fit s16 s17 s18 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 v16

  47. Outline • Server (Single Disk) • Revisiting Conventional Wisdom • Minimizing Cost • Minimizing Initial Latency • Server (Parallel Disks) • Balancing Workload • Minimizing Cost & Initial Latency • Client • Handling VBR • Supporting VCR-like Functions

  48. Unbalanced Workload Video HOT Video Cold Video Cold

  49. Balanced Workload Video HOT Video Cold Video Cold

  50. Per Stream Memory Use (Use M Disks Independently) N × TR × DR × Tseek S = TR - N × DR M × N

More Related