Download - Fast Near-Optimal Delivery of Live Streams in CDN

Fast Near-Optimal Algorithmfor Delivering Multiple LiveVideo Channels in CDNsJiayi Liu and Gwendal SimonTelecom Bretagne28/05/2013

Context : live stream delivery in CDN

Content Provider

encodersingestserver

CDN provider

originserver

edgeservers

Clients

Content provider : content generationCDN provider : content deliveryClients : content consumption

2 / 25 Jiayi Liu DASH live streaming algorithm


3-tier CDN topology (Akamai CDN delivery network)

sources

reflectors

edge servers

Phase 1 : Sources transcode streamsPhase 2 : Reflectors deliver streamsPhase 3 : Edge servers offer streams to end users



3-tier CDN topology (Akamai CDN delivery network)

sources

reflectors

edge servers

Phase 1 : Sources transcode streamsPhase 2 : Reflectors deliver streamsPhase 3 : Edge servers offer streams to end users


Current trend

Diverse user devices

video service

ADSL/FTTH 3G

WiFi


Current trend

Rate adaptive streaming (DASH standard)

video

representation 1

representation 2

...

representation n

bitrate

150 kbps

240 kbps

...

4540 kbps

bitrate

150 kbps

240 kbps

...

4540 kbps

quality

low

high


Current trend


video

representation 1

representation 2

...

representation n

bitrate

150 kbps

240 kbps

...

4540 kbps

bitrate

150 kbps

240 kbps

...

4540 kbps

quality

low

high


Current trend


video

representation 1

representation 2

...

representation n

bitrate

150 kbps

240 kbps

...

4540 kbps

bitrate

150 kbps

240 kbps

...

4540 kbps

quality

low

high


Current trend


video service

ADSL/FTTH 3G

WiFi

Req_repHD Req_rep low

Req_repmedium


Current trend


video service

ADSL/FTTH 3G

WiFi

Req_repHD Req_rep low

Req_repmedium


ChallengesDASH high aggregated video bit-rate

Netflix has 14 representations with 15 Mbps/video

Heavy transmission burden on CDNCDN can be underprovisioned

→ Challenges :live DASH streaming in under-provisioned CDN

















Outline

1. Discretized streaming capacity problem

2. A practical scenario and an algorithm

3. Evaluation

4. Conclusion


Avancement

1 Discretized streaming capacity problem

2 A practical scenario and an algorithm

3 Evaluation

4 Conclusion


Discretized streaming capacity problem

Goal : maximize the throughput of CDN

previous work : streaming capacity problemmaximizing deliverable bit-rate in P2P networkelastic video bit-rate based

our work : discretized streaming capacity problemDASH : stream bit-rate predefinedthroughput : the number delivered streamsstream utility : gain of edge server for streammaximizing the utility of delivered streams



Goal : maximize the throughput of CDNprevious work : streaming capacity problem

maximizing deliverable bit-rate in P2P networkelastic video bit-rate based











our work : discretized streaming capacity problem

DASH : stream bit-rate predefinedthroughput : the number delivered streamsstream utility : gain of edge server for streammaximizing the utility of delivered streams







Problem formulationObjective : max ∑i ,j ,e α

i ,je · x i ,j

edi ,j : i-th representation of the j-th channelx i ,j

e : indicates if edge server e receives di ,jαi ,j

e : utility of edge server e on di ,j

Problem definitionDelivery trees : TijProblem : Given the topology and capacityconstraints of a CDN, find delivery tree sets, {Tij},such that ∑i ,j,e α

i ,je · x i ,j

e is maximized.


Problem formulationObjective : max ∑i ,j ,e α

i ,je · x i ,j

edi ,j : i-th representation of the j-th channelx i ,j

e : indicates if edge server e receives di ,jαi ,j

e : utility of edge server e on di ,j

Problem definitionDelivery trees : TijProblem : Given the topology and capacityconstraints of a CDN, find delivery tree sets, {Tij},such that ∑i ,j,e α

i ,je · x i ,j

e is maximized.

ILP formulation and NP-complete complexity 1

1. Jiayi Liu and Gwendal Simon, Fast Near-Optimal Algorithm for Delive-ring Multiple Live Video Channels in CDNs, ICCCN, 2013.


Avancement



3 Evaluation

4 Conclusion


A practical scenario

CDN full connectivity

Homogeneous CDN equipments capacity C










Bottom-up tree construction

One tree per stream ; one tree per reflector

borderreflectors

edge servers

intermediatereflectors

source

To deliver di (with bit rate λi) to gi edge servers :Number of streams a node can forward : δi = bC/λicNumber of border reflectors : mi = dgi/δieNumber of intermediate reflectors : dmi −1

δi −1 e


Bottom-up tree construction

One tree per stream ; one tree per reflector

borderreflectors

edge servers


source

To deliver di (with bit rate λi) to gi edge servers :Number of streams a node can forward : δi = bC/λicNumber of border reflectors : mi = dgi/δieNumber of intermediate reflectors : dmi −1

δi −1 e


Greedy Algorithm

utility score per rate unit (uspru) : αieλi

Iterate on uspru in decreasing order

In each iteration :A uspru with a certain edge server and streamEstimate the number of reflectors neededIf the CDN can afford, continue ; else end.

Results : A set of edge servers, and number ofreflectors used in each tree


Greedy Algorithm






Greedy Algorithm






Greedy Algorithm






Analysis : approximate ratio

Wasted bandwidth for each tree :

borderreflectors

edge servers


source

Unused border reflectorcapacity

Intermediate reflectorcapacity


Analysis : approximate ratioUnused border reflectors bandwidth =

total bandwidth (miC) - used bandwidth

borderreflectors

edge servers


source

Used bandwidth ≥ (mi − 1)δiλi

C ≤ (δi + 1)λi

Unused border reflector bandwidth ≤ miλi + C17 / 25 Jiayi Liu DASH live streaming algorithm


Capacity of intermediate reflectors :

borderreflectors

edge servers


source

• Connect to borders re-flectors : miλi

• Connect to borders re-flectors : miλi• Inter-intermediate reflec-tors connection : ≤ miλi

• Connect to borders re-flectors : miλi• Inter-intermediate reflec-tors connection : ≤ miλi• Unused : ≤ C

• Connect to borders re-flectors : miλi• Inter-intermediate reflec-tors connection : ≤ miλi• Unused : ≤ C• Finally, ≤ 2miλi + C




borderreflectors

edge servers


source








borderreflectors

edge servers


source








borderreflectors

edge servers


source








borderreflectors

edge servers


source







Wasted bandwidth for each tree ≤ 3miλi + 2C

Wasted bandwidth for all trees ≤ 3Nrλ∗ + 2NchNrpC

Finally, S ≥ wastedNrC S∗ ≥ NrC−3Nrλ

∗1−2NchNrpCNrC S∗

=(1− 3λ∗

C −2NchNrp

Nr

)S∗

1. λ∗ = maxi λi







=(1− 3λ∗

C −2NchNrp

Nr

)S∗

1. λ∗ = maxi λi







=(1− 3λ∗

C −2NchNrp

Nr

)S∗

1. λ∗ = maxi λi


Avancement



3 Evaluation

4 Conclusion


Setting

3 sources

20 to 100,000 reflectors

CDN network provisioning 70%

3 channels with 5 representations each

C = 200 Mbps


EvaluationS∗ calculated based on a theoretical upper bound

Running time : less than 30 seconds

Approximate ratio : 0.978 for 200 reflectors ; 0.993 for 1000reflectors


Avancement



3 Evaluation

4 Conclusion


Conclusion

Discretized streaming model for live DASHstreaming

ILP formulation and NP-Completeness

A fast and near-optimum algorithm

Future workDefine specific utilityDistributed algorithmLive DASH streaming CDN system

