Proxy Caching For Multimedia Objects Project of Multimedia Course Amir Nayyeri

1. Proxy Caching For Multimedia Objects Project of Multimedia Course Amir Nayyeri

2. Overview • Introduction • Video Caching • Introduction • Overview of Current Algorithms • My Work • Other Interesting Subjects • Prefetching • Distributed Proxies

3. What can a Proxy Do? Reducing the delay for users. Reducing the load in the Backbone. How? Prefetching Caching Popular Requests Introduction

4. Video Stream Caching Video Objects vs Web Objects • High Data Rate, yet Adoptive • Huge Volume • One Hour MPEG1, about 675 MB • Long Playback Duration • Various Interactions • Random Access • Early Termination

5. Video Stream Caching Video Objects vs Web Objects • Caching the entire object is not feasible • Media Streams are not required to deliver at once • We can change the bandwidth for media streams, gaining lower quality • …

6. Video Stream Caching Three Primary Ideas: • Sliding Interval Caching • Prefix Caching • Segment Caching

7. r2 r1 r2 r1 2 3 1 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Video Stream Caching Sliding Interval Caching, Dan et all, 1996 • Maintain the blocks after servicing the request. • It tries to benefit from similar requests in a short period of time.

8. Video Stream Caching Prefix Caching, Sen et all. 1999 • Cache the Initial Frames of the stream • Many of the users terminate the video before reaching the end of it • The size of the video prefix • Path between the server and the proxy • Client playback delay • Aids Bandwidth Smoothing

9. Video Stream Caching Segment Caching, Wu et all, 2001 • Blocks of a media object are grouped into variable-sized, distance-sensitive segments • Two LRU stacks are maintained: one for initial segments, one for later segments • Provides better facilities for the replacement mechanism

10. Video Stream Caching Common Assumptions till NOW • Continuous Playback • No interaction with the users • Homogeneous Clients • Identical Access Bandwidth • Time Partitioning Only • Non-Adoptive Caching

11. Video Stream Caching Heterogeneous Environments • Users with different request types • From Cell Phone to PC • Maybe Different formats of the same object is requested • Can benefit from Transcoding, or Layered Coding

12. Video Stream Caching Rajaei et all, proposed the following, 2000: • On the first request of an object cache it as you receive it from the server. • On later requests try to refine your database

13. Video Stream Caching Rajaei et all, proposed the following, 2000: • If the cache is full follow this replacement strategy: • Once a victim video is identified, its cached segments are flushed as presented in the figure

14. Video Stream Caching Tang et all, proposed the following, 2002 : • They tried to benefit from transcoding • Simplicity: transcode only from the full version • FVO, Full Version Only, Fetching only the full version and transcode if other formats are required  High CPU Load • TVO, Transcoded Version Only, Fetching all the transcoded formats from the server  High Network Load • Tradeoff • Use FVO with probability p • TVO with Probability 1-p • Adjust p according to the history of the requests

15. My Work • Supporting users interactions, Random Access • Considering Segments as the caching blocks rather than entire object • Simple Version: Trying to get max Utility with limited initial tolerable delay, D, and proxy to server bandwidth, B. • Utility Function: U = (L-r1)2 + (L-r2)2 + … + (L-rn)2 ri is the layers of the segment sent to the client for request i.

16. My Work • Splitting the System of Caching into two main components, Queue Manager and Request Manager • Request Manager sends the request from the main server to the Queue Manager • Queue Manager tries to fetch more vital requests from the server sooner

17. Overall System Design Users RM QM Main Server

18. Request Manager • Maintains a vector from Recently referenced Segments • These Segments are sorted due to an estimation the probability of future accesses • The Vector is divided into N parts, p1, … pN • pi contains segments cached up to ith layer. P1 P2 … PN-1 PN

19. Request Manager • Upon a request of Seg from the clients: • Update the access record of Seg, and replace it in the vector • Send request of not cached layers to QM • Wait D • Send the ready video to the client • Start prefetching the future segments, if they are not already in the cache

20. Queue Manager • Three types of requests: • Time Limit: those that should be answered after a limited time, extra layers to cache. • Time Limit and not needed else: like number one but they are not needed after the time limit passed, extra layers to show. • Vital time limit: they should be answered on time or the system will encounter serious problems, base layer to show.

21. Existing Challenges • The replacement function: some primary assumptions are considered like using some well-known functions like LRU … • How much of the Vector should be assigned to each layer storage? • How the method can be extended to heterogeneous environments? • Theoretical analysis about the order of the algorithms and also about the exactness of the solution could be really valuable. • Maybe other things later.

22. Other Interesting Subjects • Distributed Proxies • Prefetching

23. Distributed Proxies • Some Proxies Working together • They can be very far from each other • Two main models • Centralized Manager • Completely Distributed • Each Client sends its request to the nearest proxy, then it will serve it directly, or fetch the required object from the main server or other proxies

24. Prefetching • Just a brief Overview • Trying to anticipate the future requests from the previous logs • The work of Zhong Su et al: “A Prediction System for Multimedia Prefetching in Internet”, 2000.

25. Prefetching (Cont) • Keeping logs of users Request • Predicting the probability of a special requests among the future m requests, based on the previous n requests, call it m_step n_gram model. • IDEA: Different Prediction methods of AI can be used to register better results.

26. Prefetching (Cont) • Example(3-Gram, 2-Step): Log Files: A, B, C, J, E A, B, C, E, F A, B, C, E, F B, C, D, K, A B, C, D, K, B B, C, D, F, L

27. Prefetching (Cont) • Example(3-Gram, 2-Step): Log Files: A, B, C, J, E A, B, C, E, F A, B, C, E, F B, C, D, K, A B, C, D, K, B B, C, D, F, L ProbabilityModel:

28. Prefetching (Cont) • Example(3-Gram, 2-Step): Log Files: A, B, C, J, E A, B, C, E, F A, B, C, E, F B, C, D, K, A B, C, D, K, B B, C, D, F, L ProbabilityModel:

29. Any Questions? Thanks

30. References: • [1] J. Liu, and J. Xu, “A Survey of Streaming Media Caching”, Department of ComputerScience The Chinese University of Hong Kong. • [2] S. Sen, J. Rexford, and D. Towsley, “Proxy prefix caching for multimedia streams,” in Proc. IEEE INFOCOM’99, New York, NY, Mar. 1999. • [3] S. Chen, B. Shen, S. Wee, and X. Zhang, “Adaptive and lazy segmentation based proxy caching for streaming media delivery,” Proc. NOSSDAV’03, Monterey, CA, June 2003. • [4] R. Tewari, H. M. Vin, A. Dan, and D. Sitaram, “Resource-based caching for Web servers,” in Proc. SPIE/ACM Conf. on Multimedia Computing and Networking (MMCN'98), San Jose, CA, Jan. 1998. • [5] J. M. Almeida, D. L. Eager, and M. K. Vernon, “A hybrid caching strategy for streaming media files,” in Proc. Multimedia Computing and Networking (MMCN’01), San Jose, CA., Jan. 2001. • [6] J. Kangasharju, F. Hartanto, M. Reisslein, and K. W. Ross, “Distributing layered encoded video through caches,” IEEE Trans. Computers, 51(6), pp. 622-636, June 2002. • [7] R. Rejaie, H. Yu, M. Handley, and D. Estrin, “Multimedia proxy caching mechanism for quality adaptive streaming applications in the Internet,” in Proc. IEEE INFOCOM’00, Tel Aviv, Israel, Mar. 2000. • [8] J. Liu, X. Chu, and J. Xu, “Proxy Cache Management for Fine-Grained Scalable Video Streaming,” Proc. IEEE INFOCOM'04, Hong Kong, Mar. 2004. • [9] S. Podlipnig, L. Boszormenyi, “A Survey of Web Cache Replacement Strategies”, ACM Computer Science Surveys, December 2003. • [10] X. Tang, F. Zhang, S. T. Chanson, “Streaming Media Caching Algorithms for Transcoding Proxies”, ACM Proceedings of the International Conference on Parallel Processing, 2002. • [11] S. Acharya and B. C. Smith, “Middleman: A video caching proxy server,” in Proc. NOSSDAV’00, June 2000. • [12] K. C. Tsui, J. Liu, M. J. Kaiser, “Self-Organized Load Balancing in Proxy Servers: Algorithms and Performance”, ACM Journal of Intelligent Information Systems, Volume 20, Issue 1, January 2003. • [13] Brian D. Davison. (2004) Learning Web request patterns. • [14] Z. Su, Q. Yang, H. Zhang, “A Prediction System for Multimedia Pre-fetching in Internet”, Proceedings of the eighth ACM international conference on Multimedia, 2000.