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Tradeoffs in CDN Designs for
Throughput Oriented Traffic
Minlan Yu
University of Southern California
Joint work with Wenjie Jiang, Haoyuan Li, and Ion Stoica
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Throughput-Oriented Traffic
• Throughput-oriented traffic is growing in Internet
– Cisco report predicts that 90% of the consumer traffic
will be video by 2013 (E.g., NetFlix, Youtube)
– Software, game, movie downloads
– Most are delivered by content distribution networks
Revisit CDN design choices for throughputoriented traffic
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Where is the throughput bottleneck?
Client:
Network:
Server:
Computer/access Congestions at peering Not enough resource
link too slow
and upstream links
(CPU, power, bw)
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Understanding Throughput Bottleneck
• Network bottlenecks are common
Buffering ratio
– NetFlix sees reduced video rates due to low ISP capacity
– Akamai reported bottlenecks at peering links
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3.5
3
2.5
2
1.5
1
0.5
Degraded video performance
caused by network congestion
0
2
4
6
8
10 12 14 16 18
Concurrent views (K)
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Nature of Bottleneck is Changing
• More throughput-oriented applications
– Video traffic lasts longer and has higher volume
• More elephants step on each other in the future
– Decreases the benefits of statistical multiplexing
– Introduces more challenges in bandwidth provisioning
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Improving Network Throughput
• ISP-CDNs: multiple paths and better path selections
– ISPs move up in the revenue chain to deliver content
• ISP-CDNs such as AT&T and Verizon
– Control both servers and the network
– Better traffic engineering for CDN traffic
• Existing CDNs: Deploy servers at more locations and
setting up more peering points
Peering
Question
points
1: What’s the throughput benefit of
more paths over more peering points?
……
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Improving CDN Throughput
• Highly distributed approach (e.g., Akamai)
– Many server locations, more high-throughput paths
– Higher management, replication, bandwidth cost
• More centralized approach (e.g., Limelight)
– A few large data centers with more peering points
– Lower cost due to economy of scale
More centralized
Highly distributed
Question 2: How to compare more centralized vs.
more distributed CDNs on throughput and cost?
……
Modeling CDN Design Choices
• CDNs: Increase peering points at the edge
• ISPs: Improve path selection at the core
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Increase Peering Points
• Modeling peering points (PPs)
– Increase #PPs to study throughput effect
– Pick PP locations from synthetic and real topologies
• Peering point selection
– Maximize aggregate throughput
– By assigning client locations to PPs
… and splitting traffic to different PPs
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Improve Path Selection
• Today: No cooperation (1path)
– ISPs: Shortest path routing (e.g., OSPF)
– CDNs: Select peering points to maximize throughput
• Better contracts between ISPs and CDNs (n paths)
– ISPs: Expose multiple shortest paths to CDNs (e.g.,MPLS)
– CDNs: Select peering points and paths
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Improving Path Selection
• ISP-CDNs: Optimal throughput (mcf)
– Joint traffic engineering and server selection
– Reduced to multi-commodity flow problem
• Optimization formulation
– Objectives: Max total throughput
– Subject to: Client demands & Link capacity constraints
– Variables: Peering point selection, traffic splitting on
each paths (Flow_{path, pp, client})
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An Example
Min-cut size
– improving path selection only approximates the min-cut size
– increasing #peering points essentially increases min-cut size
Capacity =2
Capacity =1
Capacity =2
• With PP2 and PP3, the maximum throughput of multiple paths is 4
(min-cut size 4)
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• Increase to 4 PPs, the min-cut size now is 8
Question 1:
What’s the benefit of path selection
over peering point selection?
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Quantify the Benefits under Various Scenarios
• Network
– Topologies: power-law, random, hierarchy, different link
density, router-level ISP topo, AS-level Internet topo
– Link capacity distribution: uniform, exp., pareto, higher
inter-AS bandwidth
• CDN peering points
– Map Akamai and Limelight server IP addresses to ASes
(collected from PlanetLab measurement at Nov. 2010)
– Randomly pick peering points for synthetic topologies
• Client demands
– Session-level traces from Conviva collected between
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Dec. 2011 and April. 2012
Multipath is better than Multiple Locations
– Power law graph (500 nodes, 997 links)
– Uniform link capacity distribution
– 200 clients at random locations
Multiple paths have little improvement
over increasing peering points
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Effect of Network Topology
– Increasing peering points are better than multipath in
most topologies
– Except star-like topology with uniform link capacity
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• The throughput from
1path to mcf increases by
110% - 584%
• The throughput from 10
PPs to 20 PPs increases by
337%
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Path selection not useful under Flash Crowd
Thpt (Path + peering
point selection)
Thpt (Peering point
selection)
Relative scaling ratio
– Conviva traces during normal and flash crowd periods
– Path selection has little benefits under normal traffic
– Path selection is worse than only peering point
selection
1.4
1.2
1
0.8
0.6
0.4
0.2
0
flash crowd
normal
5min 10min30min 1hour 2hour
Path selection interval
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More peering points always better than more paths
with long-tail Distribution of Contents
– Long-tail content distribution trace from Conviva
– With fewer replications, the throughput benefit of
multipath increases
Normalized Throughput
• Without replication the content delivery is closer to the singlesource traffic
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4
3
2
1
0
100PP,1path
10PP,mcf
10PP,1path
0.1
1
2
10
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Duplication Threshold (%)
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Takeaway 1:
CDNs only need to control the edge of the Internet to
improve the throughput.
ISP-CDNs don’t get significant benefits from controlling
the network over CDNs
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Question 2:
How to compare throughput and cost
between
more centralized vs more dist. CDNs?
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Throughput Comparison of CDNs
– Assume a fixed aggregate peering bandwidth per CDN
– A more distributed CDN achieves better throughput than
more centralized one
Throughput (K)
200
peering bw
2-3
150
4-5
6-10
100
>10
Distributed
Centralized
50
0
0
50 100 150 200 250 300 350 400
#peering links
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CDN Operation Cost
• Management cost
– At each location: electricity, cooling, equip maintenance,
and human resources
• Content replication cost
– Storage cost to replicate popular content
– Bandwidth cost to redirect traffic for rare content
• Bandwidth cost
– CDNs often pay ISPs for the bandwidth they use at the
peering points based on mutually-agreed billing model
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Different Cost Functions
• Cost as a function of bandwidth at a location
Unit price per bandwidth
– Different functions: polynomial, linear, log, exp
– Model how fast the unit cost drops with throughput
– In practice: a linear combination of different functions
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Polynomial
Linear
Log
Exponential
0.8
0.6
0.4
0.2
0
20
40
60
Throughput
80
100
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Polynomial Cost
• Dist. CDN is more expensive than Centralized one
Unit price per bandwidth
– Limelight has larger throughput at each location and
thus better scalability gains
– Same observation holds across various operational cost
functions and their combinations
Distributed
0.5
0.45
0.4
2-3
4-5
6-10
>10
0.35
0.3
Centralized
0.25
0
20
40
60
80
100 120 140
Throughput (K)
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Takeaway 2:
More distributed CDNs achieve higher throughput than
more centralized CDNs, but…
… are more expensive for same throughput
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Conclusion
• A simple model to quantify CDN design choices
– Increasing the number of peering points
– Improving path selection
– More distributed vs more centralized design
• Optimizations at the edge is enough for CDNs
– Multipath has little benefit over increasing # locations
and choosing different peering links
– There’s a tradeoff of throughput and cost among CDNs
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Thanks!
Questions?
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