1. http://dblab.usc.edu Annoucements Read the papers for next week posted on http://dblab.usc.edu/csci599 Read the papers for next week posted on http://dblab.usc.edu/csci599 http://dblab.usc.edu/csci599  A homework will be assigned on Tuesday, August 29 th, due date is September 19 th. My office hours are: My office hours are:  Tuesdays 3:20 to 5 pm  Thursdays 12:30 to 2 pm Timetable for the project: Timetable for the project:  Sept 28: 1 page description of your project  Oct 3 : Feedback on your project description  Oct 12: In class presentation of your project (5 minutes)  Oct 26: In class update of your project (15 minutes)  Nov 23: Thanksgiving recess  Nov 28 & 30: Project Presentations

2. http://dblab.usc.edu Candidate projects At the end of lecture on August 24, we discussed three different projects: At the end of lecture on August 24, we discussed three different projects: 1. Extend the current studies to consider availability of data. Current focus is on throughput, number of simultaneous displays. 2. Design, implement and evaluate replacement policies that choose victim clips and swap them out in favor of other clips. Each device should be able to make this decision independent of other devices and based on the sequence of requests issued either by itself or neighboring devices. 3. Explore role of devices configured with multiple cards. What are its benefits and costs?

3. An Evaluation of Two Policies for Placement of Continuous Media in Multi-hop Wireless Networks Shahram Ghandeharizadeh, Tooraj Helmi, Taehee Jung, Shyam Kapadia, Shahin Shayandeh Computer Science Department University of Southern California

4. http://dblab.usc.edu Outline Target environment: H2O networks Target environment: H2O networks Two placement strategy Two placement strategy  Frequency-based  Byte-hit Performance comparison: Performance comparison:  Byte-hit is superior to frequency-based 1. Maximizes number of simultaneous displays 2. More robust to error in access frequencies Conclusions and future research Conclusions and future research

5. http://dblab.usc.edu Target Environment Key characteristics: Key characteristics:  Limited by the bandwidth and radio range of wireless devices (less constrained by energy and mobility).  Management of devices is decentralized.  Each device is autonomous.  Devices cooperate when in radio-range of one another.

6. http://dblab.usc.edu Home-to-Home Online (H2O) devices collaborate to deliver continuous media: Home-to-Home Online (H2O) devices collaborate to deliver continuous media: A H2O device is a wireless device with a powerful processor and abundant amount of storage. A H2O device is a wireless device with a powerful processor and abundant amount of storage. Target Environment: H2O S. Ghandeharizadeh, H2O Clouds: Issues, Challenges and Solutions, in IEEE Pacific-Rim Conference on Multimedia, 2003.

7. http://dblab.usc.edu Home-to-Home Online (H2O) devices collaborate to deliver continuous media: Home-to-Home Online (H2O) devices collaborate to deliver continuous media: A H2O device is a wireless device with a powerful processor and abundant amount of storage. A H2O device is a wireless device with a powerful processor and abundant amount of storage. H2O Framework (Cont…) S. Ghandeharizadeh, H2O Clouds: Issues, Challenges and Solutions, in IEEE Pacific-Rim Conference on Multimedia, 2003.

8. http://dblab.usc.edu Home-to-Home Online (H2O) devices collaborate to deliver continuous media: Home-to-Home Online (H2O) devices collaborate to deliver continuous media: A H2O device is a wireless device with a powerful processor and abundant amount of storage. A H2O device is a wireless device with a powerful processor and abundant amount of storage. H2O Framework (Cont…) S. Ghandeharizadeh, H2O Clouds: Issues, Challenges and Solutions, in IEEE Pacific-Rim Conference on Multimedia, 2003.

9. http://dblab.usc.edu Home-to-Home Online (H2O) devices collaborate to deliver continuous media: Home-to-Home Online (H2O) devices collaborate to deliver continuous media: A H2O device is a wireless device with a powerful processor and abundant amount of storage. A H2O device is a wireless device with a powerful processor and abundant amount of storage. H2O Framework (Cont…) S. Ghandeharizadeh, H2O Clouds: Issues, Challenges and Solutions, in IEEE Pacific-Rim Conference on Multimedia, 2003.

10. http://dblab.usc.edu H2O Framework (Cont…) H2O devices complement existing wired infrastructure H2O devices complement existing wired infrastructure A H2O device may serve in 4 different roles: A H2O device may serve in 4 different roles: 1. Display a clip: video-on-demand 2. Store a clip for future references 3. Act as a router of data from a producer to a display 4. Combination of the above 3 simultaneously

11. http://dblab.usc.edu H2O Uses & Challenges A household may: A household may:  Access clips for entertainment, education, etc.  Publish video library (never-erase).  Establish time-shifted recording of live events: monitor the house while on travel, Memex, MyLifeBits, etc. Research challenges: Research challenges:  Privacy of user profiles and content.  Effective user-interfaces.  How to minimize loss-of-data in the presence of node removals.  Hiccup-free display:  Placement of data

12. http://dblab.usc.edu Placement of Clips Three alternative placement strategies: Three alternative placement strategies: 1. Simple 2. Halo-Clip 3. Halo-Block

13. http://dblab.usc.edu Placement of Clips Consider 9 clips sorted based on their popularity Consider 9 clips sorted based on their popularity Very popular Less popular

14. http://dblab.usc.edu Network topology

15. http://dblab.usc.edu Simple replication

16. http://dblab.usc.edu No delays when displaying Matrix Data is retrieved from local storage Data is retrieved from local storage

17. http://dblab.usc.edu Display of Tarzan incurs delays Data is streamed from the base station Data is streamed from the base station

18. http://dblab.usc.edu Streaming from base station Exhausts network bandwidth of intermediate nodes: Exhausts network bandwidth of intermediate nodes:  Prevents display of clips from the base station using intermediate nodes.  Interferes with on-going display utilizing the bandwidth of the network links.

19. http://dblab.usc.edu Streaming from base station Exhausts network bandwidth of intermediate nodes: Exhausts network bandwidth of intermediate nodes:  Prevents display of clips from the base station using intermediate nodes.  Interferes with on-going display utilizing the bandwidth of the network links.  Solution: Admission control  Limited network bandwidth provides the following simple motivation: Minimize usage of network bandwidth. How?

20. http://dblab.usc.edu Streaming from base station Exhausts network bandwidth of intermediate nodes: Exhausts network bandwidth of intermediate nodes:  Prevents display of clips from the base station using intermediate nodes.  Interferes with on-going display utilizing the bandwidth of the network links.  Solution: Admission control  Limited network bandwidth provides the following simple motivation: Minimize usage of network bandwidth. How?  Maximize references serviced using local storage of a H2O device. This is realized using smart placement algorithms.

21. http://dblab.usc.edu Simple: Frequency-based Frequency-based algorithm: Frequency-based algorithm: 1. Node i sorts clips based on its access frequency 2. Node i stores those clips with the highest access frequency. Objective: maximize the number of references serviced by the local storage of Node i. Objective: maximize the number of references serviced by the local storage of Node i.  This is a greedy objective, optimizing a local criterion. Key research question: Is assigning most frequently referenced clips to each node the best strategy? Key research question: Is assigning most frequently referenced clips to each node the best strategy?

22. http://dblab.usc.edu Simple: Byte-hit Byte-hit algorithm: Byte-hit algorithm:  Node i sorts clips based on their byte-hit value. Byte-hit of clip j is defined as its frequency of access divided by its size.  Node i stores clips with the highest byte-hit value until storage available storage is exhausted. Research question: What is the performance tradeoff between Byte-hit and Frequency-based? Which one is better under what conditions? Research question: What is the performance tradeoff between Byte-hit and Frequency-based? Which one is better under what conditions? Key finding: Byte-hit is superior to Frequency-based. Key finding: Byte-hit is superior to Frequency-based.

23. http://dblab.usc.edu Assumptions 1. Constant bit rate continuous media Alternative is variable bit rate continuous media Alternative is variable bit rate continuous media 2. Fixed network topologies 3. Placement of the base station 4. A mix of media types 5. Zipfian distribution of access to clips

24. http://dblab.usc.edu Continuous Media Display of a clip as a function of time. Display of a clip as a function of time. Constant Bit RateVariable Bit Rate Time Bytes

25. http://dblab.usc.edu Continuous Media A clip has a fixed display time. A clip has a fixed display time. Constant Bit RateVariable Bit Rate Time Bytes Clip display time

26. http://dblab.usc.edu Continuous Media A clip has a fixed size. A clip has a fixed size. Constant Bit RateVariable Bit Rate Time Bytes Clip size

27. http://dblab.usc.edu Continuous Media Average bandwidth for continuous display is clip size divided by the clip display time. Average bandwidth for continuous display is clip size divided by the clip display time. Constant Bit RateVariable Bit Rate Time Bytes Display bandwidth requirements BW = Line slope

28. http://dblab.usc.edu Time and space One may manipulate the bandwidth required to display a clip by prefetching a portion of the clip. One may manipulate the bandwidth required to display a clip by prefetching a portion of the clip. Constant Bit Rate Media Time Bytes Startup latency Prefetch portion

29. http://dblab.usc.edu Time and space One may manipulate the bandwidth required to display a clip by prefetching a portion of the clip. One may manipulate the bandwidth required to display a clip by prefetching a portion of the clip. Constant Bit Rate Media Time Bytes Startup latency Prefetch portion For a tutorial see: S. Ghandeharizadeh and R. Muntz, “Design and Implementation of Scalable Continuous Media Servers,” Parallel Computing Journal, Elsevier, Vol 24, 1998.

30. http://dblab.usc.edu Assumptions 1. Constant bit rate continuous media Alternative is variable bit rate continuous media Alternative is variable bit rate continuous media 2. Fixed network topologies 3. Placement of the base station 4. A mix of media types 5. Zipfian distribution of access to clips

31. http://dblab.usc.edu 2D topologies Grid String Graph

32. http://dblab.usc.edu Assumptions 1. Constant bit rate continuous media Alternative is variable bit rate continuous media Alternative is variable bit rate continuous media 2. Fixed network topologies 3. Placement of the base station 4. A mix of media types 5. Zipfian distribution of access to clips

33. http://dblab.usc.edu Placement of base station

34. http://dblab.usc.edu

35. http://dblab.usc.edu

36. http://dblab.usc.edu Target Environment

37. http://dblab.usc.edu Assumptions 1. Constant bit rate continuous media Alternative is variable bit rate continuous media Alternative is variable bit rate continuous media 2. Fixed network topologies 3. Placement of the base station 4. A mix of media types 5. Zipfian distribution of access to clips

38. http://dblab.usc.edu Mix of media types Database consists of two media types: Database consists of two media types:  Video clips, required bandwidth = 4 Mbps  2 hours, 2 GB  60 minutes, 1 GB  30 minutes, 0.5 GB  Audio clips, required bandwidth = 300 Kbps  4 minutes, 9 MB  2 minutes, 4.5 MB  1 minute, 2.25 MB There are C clips in the database: There are C clips in the database:  Clip i mod 6 = 0 is a 2 hour video clip  Clip i mod 6 = 1 is a 4 minute audio clip  Clip i mod 6 = 2 is a 1 hour video clip  Clip i mod 6 = 3 is a 2 minute audio clip ….

39. http://dblab.usc.edu Assumptions 1. Constant bit rate continuous media Alternative is variable bit rate continuous media Alternative is variable bit rate continuous media 2. Fixed network topologies 3. Placement of the base station 4. A mix of media types 5. Zipfian distribution of access to clips

40. http://dblab.usc.edu Zipfian distribution of access Visit http://dblab.usc.edu for code Visit http://dblab.usc.edu for codehttp://dblab.usc.edu

41. http://dblab.usc.edu System Throughput

42. http://dblab.usc.edu Key observations None of the techniques observes a linear increase in throughput. Why? None of the techniques observes a linear increase in throughput. Why? Byte-his is superior to Frequency-based. Why? Byte-his is superior to Frequency-based. Why?

43. http://dblab.usc.edu Local hit ratio Byte-hit increases the number of references serviced using the local storage of each node. Byte-hit increases the number of references serviced using the local storage of each node. This can be quantified by summing up the access frequency of clips assigned to each node: This can be quantified by summing up the access frequency of clips assigned to each node:

44. http://dblab.usc.edu Byte-hit is not Optimal Byte-hit is only a greedy heuristic. Byte-hit is only a greedy heuristic. Optimal may outperform it because Optimal considers all possible combinations of assigning clips to a node. Optimal may outperform it because Optimal considers all possible combinations of assigning clips to a node.

45. http://dblab.usc.edu Example Assume capacity of each node is 14 bytes. Assume capacity of each node is 14 bytes. Consider the shown repository: Consider the shown repository:  Byte-hit assigns clip 2 to the node, resulting in a 10% hit ratio.  Optimal and Frequency- based will assign clip 1, resulting in a 70% hit ratio. Clip-idSizeFreq Freq/siz e 1140.70.05 210.10.10 3200.20.01

46. http://dblab.usc.edu Freq-based is not optimal Assume capacity of each node is 14 bytes. Assume capacity of each node is 14 bytes. Consider the shown repository: Consider the shown repository:  Byte-hit and Optimal assign clips 2 and 3 to the node, resulting in a 60% hit ratio.  Frequency-based assigns clip 1, resulting in a 40% hit ratio. Clip-idSizeFreqFreq/size 1140.40.03 210.30.30 310.30.30

47. http://dblab.usc.edu Varying access Frequencies Experimental design: Experimental design:

48. http://dblab.usc.edu Sensitivity to access freq

49. http://dblab.usc.edu Conclusions Byte-hit is superior to Frequency-based: Byte-hit is superior to Frequency-based: 1. Increased local accesses when access frequencies are accurate. 2. More robust to changes in access frequencies. Complexity of implementing Byte-hit is almost identical to Frequency-based. Complexity of implementing Byte-hit is almost identical to Frequency-based. With a mix of demographics, Byte-hit remains superior to Frequency-based. With a mix of demographics, Byte-hit remains superior to Frequency-based.

50. http://dblab.usc.edu Future research direction Compare Byte-hit with a technique that places data with the objective to optimizes for a global criterion: Compare Byte-hit with a technique that places data with the objective to optimizes for a global criterion:  Average search size for a clip  Euclidian distance between nodes  Number of simultaneous displays

51. http://dblab.usc.edu Halo-Clip

52. http://dblab.usc.edu Halo-Clip (Cont…) Display of “Finding Nemo” incurs no delays Display of “Finding Nemo” incurs no delays

53. http://dblab.usc.edu Halo-Clip (Cont…) Streaming of “Secretary” incurs delays Streaming of “Secretary” incurs delays

54. http://dblab.usc.edu Halo-Block

55. http://dblab.usc.edu Halo-Block Display of Tarzan requires streaming Display of Tarzan requires streaming Some clients may incur delays Some clients may incur delays

56. http://dblab.usc.edu Halo-Block No delay for some clients No delay for some clients