Selecting among Replicated
Batching Video-on-Demand
Servers
Meng Guo, Mostafa H. Ammar,
Ellen W. Zegura
NOSSDAV’02, May 12-14,2002
Copyright 2002 ACM
Outline
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Introduction
Batching VOD Servers
Server Selection Algorithms
Simulation Setup
Performance Evaluation
Implementation Issues
Conclusion
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Introduction
• VoD data delivery schemes
– Periodic broadcast
– Scheduled multicast
• VoD server replication allows a VoD service to
handle a large number of clients
• Our work focuses on the design of server
selection techniques for such a replicated
service and the effect of the server selection
approach on the performance of a replicated
batching-server VoD system
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Batching VOD Servers
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Batching VOD Servers ( cont.)
• A batching VoD server operates in two
phases
– Batch scheduling
– Channel allocation
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Batching VOD Servers ( cont.)
• Batch scheduling
– First Come First Serve (FCFS)
– Maximum Queue Length first (MQL)
– Maximum Factored Queue length (MFQ)
• Select the batch with the maximum value of
– qi is the queue length
– fi is the relative frequency of the arrivals of video i
• Channel allocation
– The persistent approach
– The video patching approach
– The hierarchical multicast stream merging (HMSM)
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HMSM
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Server Selection Algorithms
• Server selection algorithm
– The Closest-server-first algorithm
– The Optimized closest-server-first algorithm
– The Register all algorithm
– The Maximum-MFQ-rank-first algorithm
– The Minimum Expected Cost (MEC) algorithm
– The Merging-Aware Minimum Expected Cost
(MAMEC) algorithm
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The Closest-server-first algorithm
• Selects the server which is closest to the
client using the network hop count
measure
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The Optimized closest-server-first
algorithm
• Selects the closest server among those
with free channels
• If no servers have free channels, the
closest server is selected
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The Register all algorithm
• Put the client request into the
corresponding queue at all of the video
servers
• When the client request is satisfied at any
one server, the request is withdrawn from
the other server queues
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The Maximum-MFQ-rank-first
algorithm
• Compute the destination queue rank at
each server and sends the client request
to the queue with the best MFQ rank
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The Minimum Expected Cost (MEC)
algorithm
• The client selects the server with smallest expected cost
• The expected cost at server i
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i is the server number
j is the videoID
mi,j is the MFQ value of video j at server i
ci is the number of free channels at server i
a is the adjust parameter which is the load balancing
threshold controller
• di is the hop count from the client to server i
• Wk’s are the weight associated with the various
• W1>>W2>W3
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The Merging-Aware Minimum
Expected Cost (MAMEC) algorithm
• The client selects the server whose merging aware
expected cost value is smallest
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ri,j is the time when the client requests video j at server i
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si,l is the starting time of the latest regular channel l which is
broadcasting video j
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avgj is the average latency when requesting video j
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B is the client side buffer size in terms of video playback time
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N is some large number
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W1>>W4>>W2>W3
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Dual server reception
• A client is allowed to receive video stream
from channels of different VoD servers
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Simulation Setup
• Simulation Environment
– Use GT-ITM transit-stub model to generate a network
topology composed of about 1400 routers
• Average degree of graph is 2.5
• Core routers have a much higher degree than edge routers
– All VoD servers have the same configuration
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M=100 video programs
Each video program has a play time of 100 minutes
Capacity of transmitting C=1000 video streams simultaneously
MFQ is used in all the servers
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as the factored queue length
» △tj is the time interval since the last time this video is
scheduled
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Simulation Setup ( cont.)
• Each client’s request can be represented
by a three tuple (requesttime,
clientID,videoID)
• Poisson arrival rate (20~110 per minute)
• The video selection probability conforms to
a Zipf distribution
• The total simulation time is one day
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Performance Metrics
• User perceived latency L is measured by the
total amount of latency experienced by the
clients over the total number of client requests
R is the set of all client requests
Lr represent the access latency for request r
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Performance Metrics
• Network bandwidth consumption B is measured
by the total amount of bandwidth used by all the
multicast channels throughout the simulation
time
|t| is the number of edges in tree t
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Performance Metrics
• Channel merge rate as the number of clients
that are merged over the total number of clients
P is the set of patching channels
A is the set of all the scheduled channels
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Performance Evaluation
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Performance Evaluation (cont.)
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Implementation Issues
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Conclusion
• With the exception of the very naive
Closest Server selection technique, server
replication can indeed be used as a way to
increase the capacity of the service
leading to improved performance
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