Dynamic Replica Placement for Scalable Content Delivery

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Dynamic Replica Placement for Scalable Content
Delivery
Yan Chen, Randy H. Katz, John D.
Kubiatowicz yanchen, randy, kubitron_at_CS.Berkeley
.EDU EECS Department UC Berkeley
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Motivation Scenario
data source
data plane
Web content server
network plane
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Goal and Challenges
Provide content distribution to clients with good
Quality of Service (QoS) while retaining
efficient and balanced resource consumption of
the underlying infrastructure

• Dynamic choice of number and location of replicas
  
• Clients QoS constraints
• Servers capacity constraints
• Efficient update dissemination
• Delay
• Bandwidth consumption
• Scalability millions of objects, clients and
  servers
• No global network topology knowledge

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Previous Work (Replica Placement)

• Focused on static replica placement
• Clients distributions and access patterns known
  in advance
• Assume global IP network topology
• Data Location via DNS-redirection
• Highly inefficient (this is a hack)
• Centralized CDN name server cannot record replica
  locations

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Previous Work (Info Dissemination)

• No inter-domain IP multicast
• Application-level multicast (ALM) unscalable
• Root maintains states for all children (Narada,
  Overcast, ALMI, RMX)
• Root handles all join requests (Bayeux)
• Root split is common solution, but suffers
  consistency overhead

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Solutions for Dissemination Tree

• Peer-to-Peer Overlay Location Services with Good
  Scalability Locality
• Simultaneous Replica Placement and Tree
  Construction

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Peer-to-peer Routing and Location Services

• Properties Needed by Tree Building Algorithms
• Distributed, scalable location with guaranteed
  success
• Search with locality
• P2P Routing and Location Services Tapestry
• CAN, Chord, Pastry insufficient locality or
  flexibility to place objects
• Http//www.cs.berkeley.edu/ravenben/tapestry

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Simultaneous Replica Placement and Tree
Construction

• Static Replica Placement IP Multicast
• Modeled as a global optimization problem
• Design a greedy algorithm with logN approximation
• Optimal case for comparison
• Dynamic Replica Placement Application-level
  Multicast
• Search for qualified local replicas first
• Place new replicas on Tapestry overlay path
• Two approaches naïve and smart
• Soft-state Tree Maintenance
• Each node only maintains states for its parent
  and direct children

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Dynamic Replica Placement naïve
data plane
s
c
network plane
Tapestry mesh
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Dynamic Replica Placement naïve
data plane
parent candidate
s
proxy
c
network plane
Tapestry mesh
Tapestry overlay path
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Dynamic Replica Placement smart
data plane
client child
s
parent
proxy
sibling
c
server child
network plane
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Dynamic Replica Placement smart

• Aggressive search
• Lazy placement

• Greedy load distribution

data plane
parent candidates
client child
s
parent
proxy
sibling
c
server child
network plane
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Evaluation Methodology

• Network Topology
• 5000-node network with GT-ITM transit-stub model
• 500 d-tree server nodes, 4500 clients join in
  random order
• Dissemination Tree Server Deployment
• Random d-tree
• Backbone d-tree (choose backbone routers and
  subnet gateways first)
• Constraints
• 50 ms latency bound and 200 clients/server load
  bound

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Four Approaches for Comparison

• Overlay Dynamic Naïve Placement (dynamic_naïve)
• Overlay Dynamic Smart Placement (dynamic_smart)
• Static Placement on Overlay Network
  (overlay_static)
• Static Placement on IP Network (IP_static)

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Number of Replicas Deployed and Load Distribution

• Overlay_smart uses much less replicas than
  overlay_naïve and very close to IP_static
• Overlay_smart has better load distribution than
  od_naïve, overlay_static and very close to
  IP_static

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Multicast Performance

• 85 of overlay_smart Relative Delay Penalty (RDP)
  less than 4
• Bandwidth consumed by overlay_smart is very close
  to IP_static and much less than overlay_naive

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Tree Construction Traffic

• Including join requests, ping messages,
  replica placement and parent/child registration

• Overlay_smart consumes three to four times of
  traffic than overlay_naïve, and the traffic of
  overlay_naïve is quite close to IP_static
• Far less frequent event than update dissemination

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Conclusions

• Peer-to-peer networks can be used to construct
  CDNs
• Dissemination Tree dynamic Content Distribution
  Network with good QoS, efficiency and load
  balancing
• P2P location service to improve scalability and
  locality
• Simultaneous dynamic replica placement and tree
  construction
• In particular
• Use Tapestry to contact nearby region of tree to
  select parent
• Lazy placement of new replicas on Tapestry
  overlay path
• Close to optimal number of replicas, good load
  distribution, low multicast delay and bandwidth
  penalty at the price of reasonable construction
  traffic

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Future Work

• Evaluate with more diverse topologies and real
  workload
• Dynamic replica deletion/migration to adapt to
  the shift of users interests
• Implementation for OceanStore, a global-scale
  persistent data storage system