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

4. Motivation Limitation of client-server system
Introduction
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

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

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

7. Data Placement Policy Block-based striping Architecture
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)

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

9. Retrieval and Transmission Scheduling
Architecture
Retrieval and Transmission Scheduling
Disk retrieval scheduling
Grouped Sweeping Scheme1 (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
Tf – macro round length
n – number of nodes within a cluster
Q – data block size
Rv – video bit-rate
1P.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

10. Retrieval and Transmission Scheduling
Architecture
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
Tg – micro round length
Tf – macro round length
g – number of GSS groups

11. Fault Tolerance Node characteristics Fault tolerance mechanism
Architecture
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 code2 (RSE)
retrieve and transmit coded data at higher data rate
recover data blocks at the receiver node
2A. J. McAuley, “Reliable Broadband Communication Using a Burst Erasure Correcting Code”, in Proc. ACM SIGCOMM 90, Philadelphia, PA, September 1990, pp. 287–306.

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

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

14. Performance Evaluation
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.

15. Performance Evaluation
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.

16. Performance Evaluation
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

17. Disk Access Bandwidth Disk retrieval round length
Performance Evaluation
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
rmin – minimum transfer rate
Q – data block size

18. Disk Access Bandwidth Example:
Performance Evaluation
Disk Access Bandwidth
Example:
Disk: Quantum Atlas 10K3
Data block size (Q): 4KB
Video bitrate (Rv): 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.
3G. Ganger and J. Schindler, “Database of Validated Disk Parameters for DiskSim”,

19. Buffer Requirement Receiver buffer requirement
Performance Evaluation
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:

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

21. System Response Time System response time Scheduling delay under GSS
Performance Evaluation
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 elsewhere4
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
4Lee, 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):

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

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)

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

25. Question & Answer Session
End of Presentation
Thank you
Question & Answer Session

26. Reliability Higher reliability achieved by redundancy Appendix
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:

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

28. Impact of Redundancy Bandwidth requirement (without redundancy)
Appendix
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
Rv – video bit-rate

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