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Reporter: 謝凱旭Advisor: 曾學文 教授

An Efficient Multicast Scheduling based on Social Network

in Data Center Networks

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Outline

• Introduction• Related work• Multicast scheduling based on social network

(MSSN)• Mathematical analysis• Experiment results• Conclusions

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Introduction

• Cloud services:– Amazon EC2, Facebook, Twitter, GFS and HDFS– Use multicasting widely

• Multicast benefits data center group – Avoid sending unnecessary duplicated packets– Reduce the task finish time of delay-sensitive applications

• But– Large and unbalanced multicast traffic in DCN

• Justin Bieber has 50,000,000 followers

– More social traffic in the world• Facebook: 22.36%

– Different types: words, voices, and videos • Complex

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Introduction

• Twitter’s DCN– Uniform Resource Locator ， URL

• Retweets• Favorites• Replies

– Engagement ： Popular weights– Aggregation ： Combined data by ID– Ingestion ： Fetch features– Scorer: Scored by feature– Partitioner: Divide data– HDFS: Store data

Tweets

ImageURLs

VideosURLs

NewsURLs

BlogURLs

URLFetch

Schedule URLs

...

Aggregation IngestionEngagement

ScorerPartitonerHDFS

...

[1] https://blog.twitter.com/2014/building-a-complete-tweet-index[2] https://blog.twitter.com/2011/spiderduck-twitters-real-time-url-fetcher

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Introduction

• For Twitter:– Open source

• Easy to fetch data

– Multicast tree = 1 user + many followers (members)• Members’ data in the same cluster • Many clusters in the same pod

• Twitter– 600 million users– Video traffic is more popular

• YouTube and Flicker• Tweet:0.2KB / Picture:0.7MB / Video:5MB • Video traffic in 2013: 1600 PB per month

Easily cause traffic congestion

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Introduction

• Real-time response is critical in social DCNs– Packet latency in DCN: 200-500 μs– Congestion: >10ms (~200X) – Less congestion is better

– Problems in DCN• A lot of multicast trees in DCN• Overlapped paths Congested• Overlapped nodes High traffic• High video traffic Traffic congestion

User1 User2

Follower1 Follower2 Follower3 Follower4

Group1

Group2

Potential Hotspots

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Related Work

• Related work– Traditional multicast scheduling in Network

• Only consider layer 3 routers• Hybrid in DCNs

– Layer 2 / Layer 3

– Multicast scheduling in DCNs– Multicast scheduling in Social DCNs

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Related Work

• Multicast Scheduling in DCNs– Congestion occurs

• The core switches at the top manage the traffic– If nodes in low level is congested, they response to core switches– Core switches allocate traffic

• Not for social network– Centralized scheduler

» Not real-time

On-Line Multicast Scheduling with Bounded Congestion in Fat-Tree Data Center Networks, IEEE Journal on Selected Areas in Communications, vol. 32, no. 1, Jan. 2014

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• Multicast Scheduling in Social DCNs

– Facebook’s 4-post DCN • In order to provide real time services for large delay-sensitive apps

– Disadvantages of 4-post architecture• The cluster size is dictated by the size of the CSW• Large switches are often oversubscribed internally

– Not all of the ports can be used simultaneously

Related Work

Facebook's data center network architecture, Optical Interconnects Conference, 2013 IEEE???? 

rack switch

cluster switch

aggregation switches

protection ring

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Related Work

• The disadvantages of multicast scheduling– DCNs

• Not for social network• Can not support real time services of delay-sensitive apps

– Social DCNs• Too many multicast groups in DCN• Overlapped paths and nodes• Need multiple group scheduling

Our MSSN

Wongyai, W.; Charoenwatana, L., "Examining the network traffic of facebook homepage retrieval: An end user perspective," Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on , vol., no., pp.77,81, May 30 2012-June 1 2012

User1 User2

Follower1 Follower2 Follower3 Follower4

Group1

Group2

Potential Hotspots

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Multicast scheduling based on Social Network (MSSN)

• MSSN– Traffic Injection– Issue Filter– Congestion Detection– Load Balance Policy

Traffic Injection

Congestion Detection

Load Balance Policy

If TrafficHotspots

> ThCongestion

Yes

No

Is Potential Hotspots?

Yes

No Traffic Congestion?

Yes No

Issue Filiter

User1 User2

Follower1 Follower2 Follower3 Follower4

Group1

Group2

Potential Hotspots

Network

m

iFlow BWSize

i

1

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• Flow Manager (FM) manages traffic in the same Pod– Node locality in a tree is 84% in the same Pod [2]

• Overlapped routing paths and nodes– High social popularity

• High degree centrality[1]• High Traffic Load

Traffic congestion

Traffic Injection

[1] A social popularity aware scheduling algorithm for ad-hoc social networks, JCSSE, 2014[2] Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling, EuroSys, 2010?????

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Potential Hotspots List Safe node Potential Hotspots Source Destination Weight (MB) Weight Tag

A B C D E F G1 1 5 1 1 5 1 N 5 1 1 5

1 2 5 1 2 5

1 4 5

2 3 0.7

2 3 0.72 4 0.7

2 4 0.72 N 0.7

K N 0.0002 K N 0.0002

K 3 0.0002

K 4 0.0002

K 2 0.0002

ThCongestion

Potential Hotspots List: B E

Network

m

iFlow BWSize

i

1

AB

C

D

...

...

User1 User2 UserK

Friend1 Friend2 Friend3 Friend4 FriendN

EF

G...

SwitchFriend

Potential Hotspots List (PHL)

NetworkBWCongestionTh

//Traffic Burst

//Overlapped nodes

Cloud Analytics for Capacity Planning and Instant VM Provisioning, TNSM, IEEE Transactions on , 2013

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(5min)

Safe node Potential Hotspots Source Destination Weight (MB) Weight Tag

A B C D E F G1 1 5 1 1 5 1 N 5 1 1 5

1 2 5 1 2 5

1 4 5

2 3 0.7

2 3 0.72 4 0.7

2 4 0.72 N 0.7

K N 0.0002 K N 0.0002

K 3 0.0002

K 4 0.0002

K 2 0.0002

ThCongestion

Potential Hotspots List: B E

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Issue Filter & Congestion Detection

1Flow },...,,{ 11211 iMMM

2Flow },...,,{ 22221 jMMM

KFlow },...,,{ 21 KkKK MMM…

B

• Consider PHL• There are common issues (tag): in these flows (20%)• FM monitors the switches which contain flows with

iM1

iM1

AB

C

D

...

...

User1 User2 UserK

Friend1 Friend2 Friend3 Friend4 FriendN

EF

G...

SwitchFriend

• If ( > )– Start Load Balance

• Else – None

iSwitchDegree NetworkBW

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Load Balance Policy

AB

C

D

...

...

User1 User2 UserK

Friend1 Friend2 Friend3 Friend4 FriendN

EF

G...

SwitchFriend

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Experiment

For real world scenario：• Use Twitter API to fetch traffic between 3/3-3/10, 2015 (17:30~16:30)• Inject to NS3

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Experiment

• For real world scenario– Multicast trees : 50~250 [1]– Members of a tree ： 3~1000 [1]– Number of Pod ： 10 [2]– Links between GS and AS ： 10Gbps– Links between AS and ES ： 1Gbps– Available link bandwidth ： 30~100% [1]

• For real time response– Total throughput / total success delivery ratio– Each success delivery ratio of different types of tweets

[1]Reliable Multicast in Data Center Networks, TC, 2014[2]3D Beamforming for Wireless Data Centers, HotNets, 2011

Experiment

Compared with BCMS: 11.30%159.04GBCompared with Twitter: 7.20%101.34GB

BCMS:6.70%

Compared with BCMS: 6.70%52774.49 tweets (pic/video/text)Compared with Twitter: 2.70%21267.33 tweets (pic/video/text)

Throughput Success Delivery Ratio

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ExperimentThroughput Success Delivery Ratio

Compared with BCMS: 4.75%27.22GBCompared with Twitter: 1.64%9.40GB

Compared with BCMS: 4.74%14824.21 tweets (pic/video/text)Compared with Twitter: 1.63%5097.78 tweets (pic/video/text)

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Experiment

Video Picture Text

Compared with BCMS: 6.24%236.90 tweets Compared with Twitter: 2.39%90.75 tweets

Compared with BCMS: 5.41%19.79 tweets Compared with Twitter: 2.11%7.72 tweets

Compared with BCMS: 4.78%37440.6 tweets Compared with Twitter: 2.11%13415.1 tweets

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Analytical Model

S17CS

AS

ES

Racks

HostSwtich

H0 H1 H3H2 H4 H5 H6 H7 H8 H10 H11 H12 H13 H14 H15

S0 S1 S2 S3 S4 S5 S6 S7

S9S8 S10 S11 S12 S13 S14 S15

S19S18S16

H9

Host

Swtich S6

S14

S0

S8

S16

S10 S12

S4 S5

H13 H1

H12

H14 H15

H10H9H8H4 H5

S17S7

S2T1

T2

Overlapped Path

• To simplify – Group number: 50 到 250– Member number: 3 到 1000– Pod number:4– Host number:40– CS,AS and AS,ES:10Gpbs– ES,ToR:1Gbps

Analytical Model-Throughput共用 Host 節點所收到的流量

共用 Switch 節點所收到的流量

每個 Multicast tree 所產生的流量

Multicast tree 的數目

Host

Swtich S6

S14

S0

S8

S16

S10 S12

S4 S5

H13 H1

H12

H14 H15

H10H9H8H4 H5

S17S7

S2T1

T2

Overlapped Path

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Analytical Model

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Analytical Model-Throughput

整體網路的產量 整體網路經過所有 Switch 的流量

所有接收端所接收到的流量總和大小 所有的 host 節點數

Switch 所接收到的流量總和大小

所有的 Switch 數

Host

Swtich S6

S14

S0

S8

S16

S10 S12

S4 S5

H13 H1

H12

H14 H15

H10H9H8H4 H5

S17S7

S2T1

T2

Overlapped Path

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Analytical Model-Throughput

Tweet 的大小 Picture 大小 Video 大小整體網路的產量

所有接收端所接收到的流量總和大小

Tweet 的大小 Picture 大小 Video 大小

Analytical Model-Standard Deviation

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節點所收到的平均產量

Switch 所收到的平均流量

For Host=9397350=8=0.2MB=22710.84bpsSimulation: 21437.72bpsError ratio=5.94%

Analytical Model-Standard Deviation

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Host 產量的標準差

整體網路 Switch 所流經的流量的標準差

Since only those shared nodes have high ||MSSN efficiently reduces ||We can get the smaller