Dynamic Replica Placement for Scalable Content Delivery

Yan Chen, Randy H. Katz,John D. Kubiatowicz

{yanchen, randy, kubitron}@CS.Berkeley.EDU

EECS DepartmentUC Berkeley

data plane

network plane

datasource

Web contentserver

CDN server

client

replica

always update

cache

Motivation Scenario

adaptivecoherence

Goal and Challenges

• 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

Provide content distribution to clients with good Quality of Service (QoS) while retaining efficient and balanced resourceconsumption of the underlying infrastructure

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

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

Solutions for Dissemination Tree

• Peer-to-Peer Overlay Location Services with Good Scalability & Locality

• Simultaneous Replica Placement and Tree Construction

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

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

parent candidate

data plane

network plane

c

s

Tapestry overlay path

Dynamic Replica Placement: naïve

proxy

Tapestry mesh

data plane

network plane

c

sproxy

Tapestry overlay path first placement choice

parent candidate

Dynamic Replica Placement: naïve

Tapestry mesh

Dynamic Replica Placement: smart

parent candidates

• Aggressive search

data plane

network plane

c

s parent

siblingserver child

proxy

Tapestry overlay path

client child

• Greedy load distribution

Dynamic Replica Placement: smart• Aggressive search • Lazy placement

• Greedy load distribution

data planeparent candidates

network plane

c

s parent

siblingserver child

proxy

Tapestry overlay path

client child

first placement choice

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

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)

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

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

Tree Construction TrafficIncluding “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

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

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