Minimizing Server Throughput for Low-Delay Live Streaming ...€¦ · Minimizing Server Throughput for Low-Delay Live Streaming in Content Delivery Networks F.Zhou,S.Ahmad,E.Buyukkaya,

Minimizing ServerThroughput for Low-DelayLive Streaming in ContentDelivery Networks

F. Zhou, S. Ahmad, E. Buyukkaya,R. Hamzaoui and G. Simon

Live Stream Delivery

Content Provider

encodersingestserver

CDN

originserver

edgeservers Clients

Recent large-scale live video streaming failedSuperbowlKorean Telecom and smart TVs

2 / 13 Gwendal Simon Minimizing Server Throughput in CDN

Live Stream Delivery

Content Provider

encodersingestserver

CDN

originserver

edgeservers Clients

Recent large-scale live video streaming failedSuperbowlKorean Telecom and smart TVs


Motivations

Where is the bottleneck in today’s CDN ?

ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers

last-mile ?last-mile ?

adaptive streaming → last-mile

peering link ?


peering link ?

adaptive streaming → last-mileedge servers in ISP → peering link

infrastructure ?



Motivations


ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers last-mile ?

last-mile ?


peering link ?


peering link ?


infrastructure ?



Motivations


ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers

last-mile ?

last-mile ?


peering link ?


peering link ?


infrastructure ?



Motivations


ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers



peering link ?


peering link ?


infrastructure ?



Motivations


ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers



peering link ?


peering link ?


infrastructure ?



Motivations


ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers



peering link ?


peering link ?


infrastructure ?



The upload capacity equipment bottleneck

2CPU

NIC

6

4 3 5

2 ⇓ ∞ ⇑

∞ ∞ ∞


The upload capacity equipment bottleneck

2CPU

NIC

6

4 3 5

2 ⇓ ∞ ⇑

∞ ∞ ∞


Our proposalno cooperation

cooperation between nodes

Objectives :minimizing source throughputmaintaining a low transmission delay


Our proposalno cooperation cooperation between nodes



Our proposalno cooperation cooperation between nodes



Rateless codes

encoder decoder

one chunkk symbols

infinite nb. ofencoded symbols k + ε symbols k decoded symbolsone chunk

k symbolsinfinite nb. of

encoded symbols k + ε symbols k decoded symbolsone chunkk symbols




infinite nb. ofencoded symbols k + ε symbols k decoded symbols

Main advantages :adaptive : no fixed code ratelow complexitysuitable for multi-source systems (Wu’2008)


Rateless codes

encoder decoder

one chunkk symbols







infinite nb. ofencoded symbols k + ε symbols k decoded symbols

Main advantages :adaptive : no fixed code ratelow complexitysuitable for multi-source systems (Wu’2008)


Multi-tree delivery (1/2)

Main objective for the delivery of one video chunk :minimize the number of trees (packets)

Main constraint in the trees :each node should be in K treesupload capacity constraint c on nodes

s

1

2

3

4

s

1

2 3

s

2

1 3

4

s

3

4 1

2

s

4

K = 3, c = {2, 2, 3, 1}



Main objective for the delivery of one video chunk :minimize the number of trees (packets)

Main constraint in the trees :each node should be in K treesupload capacity constraint c on nodes

s

1

2

3

4

s

1

2 3

s

2

1 3

4

s

3

4 1

2

s

4

K = 3, c = {2, 2, 3, 1}



Additional constraints

Do not introduce much delaysum of delays over all paths in any tree below D

Do not introduce much packet lossoverall probability of all paths in any tree below P


Our contributions

Two algorithms :1. without last constraints :

an optimal O(Kn2) algorithm2. with limited tree height :

an efficient O(Kn3) heuristic

Algorithm performances depend on the context :over-provisioned system

“source rate = video rate” is achievableunder-provisioned system

source has to compensate missing resources


Our contributions

Two algorithms :1. without last constraints :

an optimal O(Kn2) algorithm2. with limited tree height :

an efficient O(Kn3) heuristic

Algorithm performances depend on the context :over-provisioned system

“source rate = video rate” is achievableunder-provisioned system

source has to compensate missing resources


Simulations

Video and rateless code settings :H.264 with bitrates from 320 kbps to 3.2 MbpsOne chunk is one GOP : 0.5 secondsOne UDP packet is 1,000 bytesRaptor code with redundancy 5%

Network and node settings :from 5 to 25 nodesupload capacity follows log-normal distribution

mean capacity is {512, 1,024, 2,048} kbpshomogeneous packet loss probability and RTT


Scalability

5 10 15 20 250

1,000

2,000

3,000

4,000

5,000

number of nodes

transmiss

ionrate

(inkbps) 512 kbps


Scalability

5 10 15 20 250

1,000

2,000

3,000

4,000

5,000

number of nodes

transmiss

ionrate

(inkbps) 512 kbps

1024 kbps


Scalability

5 10 15 20 250

1,000

2,000

3,000

4,000

5,000

number of nodes

transmiss

ionrate

(inkbps) 512 kbps

1024 kbps2048 kbps


Decoding lag

5 10 15 20 250

200

400

600

800

1,000

number of nodes

playback

lag(in

ms)

512 kbps1024 kbps2048 kbps


Future works

Real implementation. We currently have :a fully-developed program that just workssome contacts with a small CDN company

Academic work :resource management for multiple flowsmore dynamic algorithms


Documents

Minimizing Server Throughput for Low-Delay Live Streaming ...€¦ · Minimizing Server Throughput for Low-Delay Live Streaming in Content Delivery Networks F.Zhou,S.Ahmad,E.Buyukkaya,