SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network Yan Chen, Randy H. Katz, John...
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Transcript of SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network Yan Chen, Randy H. Katz, John...
SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network
Yan Chen, Randy H. Katz,John D. Kubiatowicz
{yanchen, randy, kubitron}@CS.Berkeley.EDU
EECS DepartmentUC Berkeley
Outlines
• Motivation
• Goal and Challenges
• Previous Work
• SCAN Architecture and Components
• Evaluation Methodology
• Results
• Conclusions
Motivation Scenario: World Cup 2002
Goal and Challenges
• Dynamic choice of number and location of replicas – Clients’ QoS constraints: latency, staleness– Servers’ capacity constraints
• Efficient resource consumption– Small delay, bandwidth consumption for replica update– Small replica management cost
• Scalability: millions of objects, clients and servers
• No global network topology knowledge
Provide content distribution to clients with good latency and staleness, while retaining efficient and balanced resourceconsumption of the underlying infrastructure
Previous Work• Replica Placement
– Research communities: optimal static replica placement• Assume clients’ distributions, access patterns & IP topology• No consideration for clients’ QoS or servers’ capacity
constraints
– CDN operators: un-cooperative, ad hoc placement • Centralized CDN name server cannot record replica locations
– place many more than necessary (ICNP ’02)
• Update Multicast– No inter-domain IP multicast– Most application-level multicast (ALM) unscalable
• Split root as common solution, suffers consistency overhead
adaptivecoherence
data plane
network plane
datasource
Web contentserverCDN server
client
replica
always updatecache
SCAN: Scalable Content Access Network
DOLR mesh
Components of SCAN• Decentralized Object Location & Routing (DOLR)
– Properties needed• Scalable location with guaranteed success
• Search with locality
– Improve the scalability of d-tree: each member only maintains states for its parent and direct children
• Simultaneous Dynamic Replica Placement and d-tree Construction– Replica search: Singular, Localized or Exhaustive– Replica placement on DOLR path: Lazy or Eager
parent candidate
data plane
network plane
c
s
DOLR path
Replica Search
proxy
DOLR mesh
• Singular Search
Replica Search
parent candidates
• Localized search
data plane
network plane
c
s parent
siblingserver child
proxy
DOLR path
client child
• Greedy load distribution
DOLR mesh
data plane
network plane
c
sproxy
DOLR path first placement choice
Replica Placement: Eager
DOLR mesh
Replica Placement: Lazy
data plane
network plane
c
sproxy
DOLR path
client child
first placement choice
DOLR mesh
Evaluation of Alternatives• Two dynamic overlay approaches
– Overlay_naïve: Singular search + Eager placement– Overlay_smart: Localized search + Lazy placement
• Compared with static placement + IP multicast– Overlay_static: With global overlay topology– IP_static: With global IP topology (ideal)
• Metrics– Number of replicas deployed, load distribution– Multicast performance: Relative Delay Penalty (RDP)
and bandwidth consumption– Tree construction traffic (packets and bandwidth)
Methodology• Network Topology
– 5000-node network with GT-ITM transit-stub model– SCAN nodes placed randomly or on transit nodes
• NS-like Packet-level Network Simulations • Workloads
– Synthetic flash crowd: all clients access a hot object in random order
– Real Web server traces: NASA and MSNBC
Web Site Period Duration # Requests # Clients # objects
MSNBC 8/2/1999 10–11am 1.6M 140K 4186
NASA 7/1/1995 All day 64K 5177 3258
Methodology: Sensitivity Analysis• Various Client/Server Ratio• Various Server Density• Various Latency & Capacity Constraints• Various Network Topologies
– Average over 5 topologies with different setup
• All Have Similar Trend of Results– Overlay_smart has close-to-optimal (IP_static)
number of replicas, load distribution, multicast performance with reasonable amount of tree construction traffic
Number of Replicas Deployed and Load Distribution
• Overlay_smart uses only 30-60% of replicas than overlay_naïve and very close to IP_static• Overlay_smart has two times 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 is only 1/3 of bandwidth by overlay_naive
Tree Construction TrafficIncluding “join” requests, “ping” messages, replica
placement and parent/child registration
• Overlay_smart consumes 3 - 4 times of traffic than overlay_naïve, and the traffic of overlay_naïve is quite close to IP_static• Far less frequent event than access & update dissemination
Conclusions• P2P networks can be used to construct CDNs• SCAN: Scalable Content Access Network with
good QoS, efficiency and load balancing– Simultaneous dynamic replica placement & d-tree
construction – Leverage DOLR to improve scalability and locality
• In particular, overlay_smart recommended– Localized search + Lazy placement– Close to optimal number of replicas, good load
distribution, low multicast delay and bandwidth penalty at the price of reasonable construction traffic
Results on Web Server Traces
• Limited simulations, most URLs have very few requests • Overlay_smart uses only one third to half replicas than overlay_naïve for hot objects
data plane
network plane
datasource
Web contentserver
CDN server
client
replica
always update
cache
SCAN: Scalable Content Access Network
adaptivecoherence
DOLR mesh
parent candidate
data plane
network plane
c
s
DOLR path
Replica Search
proxy
DOLR mesh
• Singular Search
Replica Search
parent candidates
• Localized search
data plane
network plane
c
s parent
siblingserver child
proxy
DOLR path
client child
• Greedy load distribution
data plane
network plane
c
sproxy
Tapestry overlay path first placement choice
parent candidate
Dynamic Replica Placement: naïve
Tapestry mesh
• Singular Search • Eager Placement
Dynamic Replica Placement: smart• Localized 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