1. A Server-less Architecture for Building Scalable, Reliable, and Cost-Effective Video-on-demand Systems Raymond Leung and Jack Y.B. Lee Department of Information Engineering The Chinese University of Hong Kong

2. Contents Introduction Server-less Architecture Performance Evaluation System Scalability Summary

3. Client-Server Architecture Traditional client-server architecture clients connect to server for streaming system capacity limited by server capacity Introduction

4. Motivation Limitation of client-server system system capacity limited by server capacity high-capacity server is very expensive Availability of powerful client-side device, or called set- top box (STB) home entertainment center - VCD/DVD player, digital music jukebox, etc. relatively high processing capability, and local HD storage Server-less architecture eliminates the dedicated server each user node (STB) serves both as a client and as a mini- server fully distributed storage, processing, and streaming Introduction

5. Server-less Architecture Basic principles dedicated server is eliminated users are divided into clusters video data is distributed to nodes in a cluster Architecture

6. Challenges Data placement policy Retrieval and transmission scheduling Fault tolerance Distributed directory service System adaptation and dynamic reconfiguration etc. Architecture

7. Data Placement Policy Block-based striping video data is divided into fixed-size blocks and then distributed among nodes in the cluster low storage requirement, load balanced capable of fault tolerance using redundant unit(s) Architecture

8. Retrieval and Transmission Scheduling Round-based Schedulers retrieves data block in each micro-round transmission starts at the end of micro-round Architecture

9. Retrieval and Transmission Scheduling Disk retrieval scheduling Grouped Sweeping Scheme 1 (GSS) able to control the tradeoff between disk efficiency and buffer requirement Transmission scheduling Macro round length time required that every node sends out a data block of Q bytes depends on system scale, data block size and video bitrate T f – macro round length n – number of nodes within a cluster Q – data block size R v – video bit-rate 1 P.S. Yu, M.S. Chen & D.D. Kandlur, “Grouped Sweeping Scheduling for DASD-based Multimedia Storage Management”, ACM Multimedia Systems, vol. 1, pp. 99 –109, 1993 Architecture

10. Retrieval and Transmission Scheduling Transmission scheduling Micro round length under the GSS scheduling, the GSS group duration within each macro round depends on macro round length and number of GSS groups T g – micro round length T f – macro round length g – number of GSS groups Architecture

11. Fault Tolerance Node characteristics lower reliability than high-end server shorter mean time to failure (MTTF) system fails if any one of the nodes fails Fault tolerance mechanism erasure correction code to implement fault tolerance Reed-Solomon Erasure code 2 (RSE) retrieve and transmit coded data at higher data rate recover data blocks at the receiver node 2 A. J. McAuley, “Reliable Broadband Communication Using a Burst Erasure Correcting Code”, in Proc. ACM SIGCOMM 90, Philadelphia, PA, September 1990, pp. 287–306. Architecture

12. Fault Tolerance Redundancy encode redundant data from video data recover lost data in case of node failure(s) Architecture

13. Performance Evaluation Storage capacity Network capacity Disk access bandwidth Buffer requirement System response time Performance Evaluation

14. Storage Capacity What is the minimum number of nodes required to store a given amount of video data? For example: video bitrate: 150 KB/s video length: 2 hours storage required for 100 videos: 102.9GB If each node can allocate 1GB for video storage, then 103 nodes are needed (without redundancy); or 108 nodes are needed (with 5 nodes added for redundancy) This sets the lower limit on the cluster size. Performance Evaluation

15. Network Capacity How many nodes can be connected given a certain network switching capacity? For example: video bitrate: 150KB/s If the network switching capacity is 32Gbps, and assume 60% utilization up to 8388 nodes (without redundancy) Network switching capacity is not a bottleneck. Performance Evaluation

16. Disk Access Bandwidth Recall the retrieval and transmission scheduling: Continuous data transmission constraint: must finish retrieval before transmission in each micro-round need to quantify the disk retrieval round length and verify against the above constraint Performance Evaluation

17. Disk Access Bandwidth Disk retrieval round length time required retrieving data blocks for transmission depends on seeking overhead, rotational latency and data block size suppose k requests per GSS group Continuous data transmission constraint: – maximum retrieval round length -- fixed overhead – maximum seek time for k requests W -1 – rotational latency r min – minimum transfer rate Q – data block size Performance Evaluation

18. Disk Access Bandwidth Example: Disk: Quantum Atlas 10K 3 Data block size (Q): 4KB Video bitrate (R v ): 150KB/s Number of nodes: N GSS group number (g): N (reduced to FCFS scheduling) Micro round length: Disk retrieval round length: 0.017s < 0.027s Therefore the constraint is satisfied even if FCFS scheduler is used. 3 G. Ganger and J. Schindler, “Database of Validated Disk Parameters for DiskSim”, http://www.ece.cmu.edu/~ganger/disksim/diskspecs.html Performance Evaluation

19. Buffer Requirement Receiver buffer requirement double-buffering scheme: one for storing data received from the network plus locally retrieved data blocks another one for video decoder Sender buffer requirement under GSS scheduling: Performance Evaluation

20. Buffer Requirement Total buffer requirement versus system scale Data block size: 4KB, Number of GSS groups: g=N Performance Evaluation

21. System Response Time System response time time required from sending out request to playback begins scheduling delay + pre-fetch delay Scheduling delay under GSS time required from sending out request to data retrieval starts can be analyzed using urns model detailed derivation available elsewhere 4 Prefetch delay time required from retrieving data to playback begins one micro round to retrieve a data block and one macro round to transmit the whole block to the client node 4 Lee, J.Y.B., “Concurrent push-A scheduling algorithm for push-based parallel video servers”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 9 Issue: 3, April 1999, Page(s): 467 -477 Performance Evaluation

22. System Response Time For example: Data block size: 4KB Performance Evaluation

23. System Scalability Not limited by network or disk bandwidth prefers FCFS disk scheduler over SCAN Limited by system response time prefetch delay increases linearly with system scale example: response time of 5.615s at a scale of 200 nodes Solution forms new clusters to expand system scale uses smaller block size (limited by disk efficiency) System Scalability

24. Summary Server-less architecture proposed for VoD dedicated server is eliminated each node serves as both a client and a mini-server inherently scalable Challenges addressed: data placement policy retrieval and transmission scheduling fault tolerance Performance evaluation acceptable storage and buffer requirement scalability limited by system response time Summary

25. End of Presentation Thank you Question & Answer Session

26. Reliability Higher reliability achieved by redundancy each node has independent failure and recovery rate, and respectively let state i be the system state where i out of the N nodes failed at state i, the changing rate to state (i+1) and (i-1) are and respectively assume the system can tolerate up to h failures using redundancy the system state diagram is shown as follows: Appendix

27. Reliability System mean time to failure (MTTF) can be analyzed by continuous time Markov Chain model solving the expected time from state 0 to state (h+1) in previous diagram, Appendix

28. Impact of Redundancy Bandwidth requirement (without redundancy) (N-1) received from network and one locally retrieved from disk Bandwidth requirement (with h redundancy) additional network bandwidth will be needed for transmitting the redundant data R v – video bit-rate Appendix

29. Impact of Redundancy Data block size (without redundancy) block size: Q bytes Data block size (with h redundancy) block size: Appendix