QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate- based...
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Transcript of QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate- based...
![Page 1: QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate- based Networks Bin Wang and Jennifer C.Hou.](https://reader033.fdocuments.net/reader033/viewer/2022051620/56649f395503460f94c5701e/html5/thumbnails/1.jpg)
QoS-Based Multicast Routing for Distributing Layered Video to
Heterogeneous Receivers in Rate-based Networks
Bin Wang and Jennifer C.Hou
![Page 2: QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate- based Networks Bin Wang and Jennifer C.Hou.](https://reader033.fdocuments.net/reader033/viewer/2022051620/56649f395503460f94c5701e/html5/thumbnails/2.jpg)
Goal:• QoS requirements of heterogeneous receivers,
including bandwidth and delay;• Highest receiving quality for receivers• Minimize the total network resource consumptionSolution:• Source:Layered encoding(cummulative)• Receivers: tradeoff between video quality and
available bandwidth• Scheduling: rate-based link scheduling• Tree construction on weighted digraph G=(V,E)
using the global state and an auxiliary routing table
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Global stateLink state:• Available bandwidth: b(l), b:ER+,the link
bandwidth function,• Constant delay: dl,which depends on the
capacity,the propagation delay, and the maximum packet size
• Link costNode state: available buffer…Global state:• The collection of the local node/link state of all
the nodes in the network• Maintained by every node in the network
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The Auxiliary Routing Table
T is a |V| X H matrix, recording a h-hop maximum bandwidth path :
• P: path
• bw:maximum bandwidth on P
• Neighbour:next hop
• dh=sum(dl): end-end constant delay
*every node maintains a T
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Rate-based Scheduling Algorithms
• Algorithms: Generalized Processor Sharing,Weighted Fair Queuing,Virtual Clock…
• Traffic model: leaky bucket (R,sigma)
• End-end delay bound on P:
D(r,P)=(sigma+|P|*c)/r+sum(dl)
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Traffic Model for Layered Video
• Each layer: leaky bucket(R,Sigma)
• Video signal: (Ri,sigmai), i:1~m (#of layers)
• Layer k: (Rk,sigmak),
Rk=sumj=1k(Rj)
sigmak=sumj=1k(sigmaj)
• Layers are selectively forwarded on links
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Problem FormulationThe one-to-many multicast video distribution
session:
s:source
d={j|j=1~n}: receivers
{Dj|j=1~n}:delay requirements
{Rjr|j=1~n}:maximum acceptable rates,
layer-k receiver j: Rk<=Rjr<Rk+1
How to construct a tree?
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Algorithm Overview• Starting from a tree with only s• Higher-layer receiver i first• Select the most appropriate path P from T• A setup message is sent to i along P, carrying the
data structure RECEIVER and D(delay)• RECEIVER is updated by intermediate nodes, if
better path is available• Next off-tree receiver j is selected by i• A fork message is sent from i• A finish message is sent to s if no off-tree node
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The RECEVIER data structureRECEIVER.RECEIVER[i] records the least-
hop appropriate path P for receiver i:
• OnTreeNode: initialized to s
• path: P, with sufficient bandwidth
• r: the minimum bandwidth ri for delay
• cost: |P|*r, the total bandwidth due to receiver i(only for new branch)
• Rr: maximum acceptable rate Rri
• level: # of layers
• tag: on-tree or off-tree
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Path Selection from T• Calculate the minimum bandwidth ri
according to deley requirement:
ri>=(sigmak+|p|*c)/(Di-sum(dl))
• Select the least-hop path with T(i,h).bw>=max(ri,Rk)
• No loop
• Reserved bandwidth: max(ri,Rk)
• if no path exists,or ri>Rri, degrading layer
(Lower cost? Best path?)
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Next Off-Tree Receiver Selection Higher layer & Smaller cost node first:• Gk+1=…=Gm=0, Gk<>0 • Select the receiver i from Gk with min(|P|
*ri)• RECEIVER[i].tag=true• A setup message is sent to i • i will select next receiver j• i sends a fork message to
RECEIVER[j].OnTreeNode
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Path UpdateIntermediate nodes update D&RECEIVER:
• Delay requirement (D:cumulative delay) Di>=D+(sigmak+|p|*c)/ri+sum(dl)
• Select the minimum-hop path P from T(first entry T(i,h))
• Smaller cost(total bandwidth): |P|*ri
• Update RECEIVER for every receiver i if smaller cost
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Dynamic Receiver Join/LeaveGoal: seamless transition via incremental
changing
Leave:
• Leaf node: leave message is sent upstream,and resource is released by a fork node
• Non-leaf node: just relay incoming downstream messages
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Dynamic Receiver Join/Leave(cond.)
Join:• Join request to s with di&Ri
r
• S multicasts a join message with RECEIVER[i]&D to all(?) on-tree receivers
• Intermediate nodes updates D,and RECEIVER if smaller cost path available
• The leaf receivers send back RECEIVER• S select a fork node with least cost• fork message(Why not use updated T? Least cost?)
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Auxiliary Routing Table T UpdateCompute the h-hop maximum bandwidth paths from
the current node to all the other nodes:iterate H times, h=1~H
• Update T(j,h)(j=1~|V): for every neighbour u of j, if no loop
T(j,h).bw=max(T(j,h).bw,min(T(u,h-1).bw,b(u,j)))
• If loop exists(j in T(u,h-1).P), recursively calculate a new T(u,h-1) excluding j
• For complexity, excluding u if loop or limit the scope of recursion
• Run off-line and infrequently
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Complexity
• # of messages: O(2*|d|)• T update: exponential in the worst case If bapassing the loop: Check every neighbour u of j: O(|V|) Check loop and bw: O(H)+1 Run H times for every receiver: O(H)*O(|V|) So O(H2*|V|2)
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Simulation• Topology: vBNS , switch cluster,random network(Waxman
method, which can obtain “real world” networks)• Simulator: NetSimQ
• Comparing: maximum bandwidth tree algorithm Maxemchuk’s algorithm • Varing parameters: lambda(session arrival rate),|
d|,Dj• Performance metrics: total bandwidth required,
percentage of receivers attaining QoS
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Maxemchuk’s Algorithm• Minimize bandwidth consumption without
considering QoS requirement• Use modified T-M heuristic• A variant of steiner tree problem: construct
a minimum cost tree for a subset of nodes, with link cost fixed in the network• Link cost: basic cost *highest reserved rate• Construct from higher-rate receivers and
then add lower-rate of receivers• No explicit QoS consideration• Centralization
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Max Bandwidth Tree Algorithm• For Layered-encoded data (cumulative)
• Compute the maximum available bandwidth tree to connect all receivers,receivers are classified by receiving capabilities
• Minimize the sum of satisfaction level
• For shorter path:select the node nearest to source (not guarantee shortest path)
• For bandwidth saving: reduce bandwidth from the receivers
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Simulation results
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Simulation results(cond)
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Simulation results(cond)
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Simulation results(cond)
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Issues:• The original Goal is achieved
• Shortest path? Smallest total cost?The best path?
• Complexity (scalability?):
Global state
T update
Link state update
Complexity!
• A good attemption!
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