End-to-End Analysis of Distributed Video-on-Demand Systems Padmavathi Mundur, Robert Simon, and Arun K. Sood IEEE Transactions on Multimedia, February 2004

Outline Motivation Hierarchical VoD architecture Analytical model Evaluation methodology and results Conclusion

Motivation In a real environment, if a video requires R mbps transmission rate, allocate R mbps bandwidth is not accurate enough From network view, analyze the bandwidth required for videos

Hierarchical VoD architecture

Data flow at server Double buffer technique RSVP

Disk scheduling and double buffer scheme in the server RAID-5 storage 2. SCAN EDF scheduling (RSVP) Token bucket + WFQ

Traffic regulator at server (1/2) Leaky bucket  Control average rate send pkts packets wait packets to network r pkts/sec

Traffic regulator at server (2/2) Token Bucket  Control average rate  Control input burst size remove token packets wait packets to network r tokens/sec buckets holds up to b tokens

Weighted fair queuing (WFQ) at server Provide different priority to different packets

Combine token bucket & WFQ Token bucket scheme controls the average output rate WFQ allocates different resource to different users Token bucket + WFQ  provide delay upper bound

Resource reservation protocol (RSVP) along the routing path

Review the whole data flow RAID 5 storage SCAN EDF scheduling Double buffer technique Token bucket + WFQ RSVP

Admission control scenario Remote cluster Remote cluster Local cluster Local distribution network Network connections request Disk admission control Check available bandwidth

Analysis – admission control Server disk , if accept the request Network  overall disk bandwidth client playback rate bandwidth available on jth link reserved rate client playback rate

Analytical model – use delay bound to calculate reserved bandwidth WFQ + Token bucket rJrJ bJbJ wJwJ r1 b1 w1 …… : max packet size for the flow : MTU : retrieval block size

Performance evaluation – request handling policy Redirect:  A blocked request at one resource is simply redirected to other resources Split-based  Sharing the loads to other resources

Simulation setup – environment Remote cluster1 Remote cluster2 Local cluster Network1 Network2 requests Servers in local cluster: 5 Storage capacity per local server: 500 GB Disk transfer rate at local server: 1.2 Gbps Hops to remote cluster1: 3 Hops to remote cluster2: 6 Max. Transmission Unit: 1500 Bytes Maximum packet size: 1500 Bytes Network bandwidth: 2488 Mbps End-to-end delay 300 ms Size of video collection 150 Size of videos in GBytes: 2.46 to 4.8 Service time in hours: 0.68 to 2.03 Video popularity: according to Zipf distribution Request arrival interval: adopt Poisson distribution

Simulation setup – request handling policies Redirect  Redirect order: LC  RC1  RC2 Split  Split50-60: 50% are served in LC, 60% of the remains are served in RC1, the rests are in RC2 Split-redirect  Split first, also contains redirect policy

Simulation setup – scenarios Replicated video collection (RVC)  All videos are available on local or remote servers Distributed video collection (DVC)  Only a partial set of videos is available on the local cluster, the requests for non-available parts are served by remote clusters

Simulation results – compare performance of request handling policies in RVC Purpose: test the performance of the VoD system using different request handling policies Redirect policy performs better than the other two policies

Simulation results – difficulties with split- based policies in RVC The lines are crossed over in the previous figures (Ex: split and split-60-60) It is difficult to pick an efficient split for a given workload Split performs better at low load Split performs better at heavy load

Simulation results – performance at each resources for split policies in RVC Use individual resource performance to help explain the crossover and divergence behavior

Simulation results – efficient split policy in RVC Split requests proportional to their resource It may difficult to know remote clusters since they may be dynamically shared with other user populations

Simulation results – varying the number of videos on local server in DVC local storage size local video number Distribute the available storage capacity at the local cluster to videos in proportion to their popularity Redirect policy only Class1: top 20% popular, class2: 20~60% popular, class3: last 40% popular

Conclusion and distribution Develop a method to analyze distributed VoD systems Use an extensive simulation to the distributed VoD architecture and evaluate several request handling policies