greening the internet with nano data...

Greening the Internet with Nano Data Centers

V. Valancius (Georgia Institute of Technology), N. Laoutaris (Telefonica Research), L. Massoulie, C. Diot (Thomson), P. Rodriguez (Telefonica Research)

Presented by Sean Barker

2

Gateway vs. Set-top-box

Internet

Home gateway• Internet connection• Traffic management

Set-top-boxes• Content processing

3

Gateway as new center of media experience

The Internet

Content & ControlServers

Internet services

Extended home network

Homenetwork

4

The current model: Data centers

The Internet

Data

Centers

.

.

.DSLAM

Home boxes

5

Limitations

Expensive – High capital investment– Customer generally pays per byte

Location constraints in order to be “central” Requires a lot of redundancy to be robust– Electricity shortage– Content availability

Power, power, power New service deployment is slow– ISPs not encouraged to take risks, nor to deploy new services

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The nano data center

Take advantage of always-on gateways

Add memory and stronger CPU to home gateways

Push content to gateways when bandwidth is cheap

Manage millions of gateways as a logical ‘single server’ using P2P infrastructure

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The nano data center model

The Internet

Video server

Control server

.

.

.DSLAM

Home boxes

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The nano data center

PROS– Multiple applications can take

advantage of the model (VoD, gaming, catchTV, UGC)

– ISP friendly– Reduces traffic volumes and

variability on backbones.– Highly scalable and robust by

design– Cheap for ISPs– Flexible for users– Localized & personalized

services

CONS– Uplink bandwidth often limited– Millions of boxes to manage

using P2P– Cost of gateway– Incentive?– Privacy?– Always on?

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Gateway uptime (*)

(*) Courtesy of Krishna Gummadi

More than 60% of gateways are up more than 80% of the time

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Push phase

Video content server

Control server

.

.

.DSLAM

RHGs

11

Pull phase

Control server

.

.

.DSLAM

RHGs

Video content server

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Placement strategy

……

1

Data window

… 22 1M

…

… 2 M 1 M3 3 3 ……

Movie

11… 1 22 2 33 3 MM…M

1 22 1K

…

2 K 1 K3 3 3 …… ……

Erasure codes(e.g., LDPC)

gateways

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Placement strategy

Replicate content according to popularity

Popular content served by gateways– slack bandwidth from original content servers

Number of replicas determined by solving optimization problem

– Constraints on available upload and storage, number of clients, request rates, etc.

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Popularity aware placement

Partition content into hot / warm / cold categories• Hot: replicate on all gateways• Warm: use code-based placement• Cold: no proactive placement (stays on servers)

hotwarm

cold

b/wmemory

popularity

movies

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Where should NaDa save energy?

The Internet

Data

Centers

.

.

.DSLAM

Home boxes

Data Centers: capital cost, redundancy,

cooling

Internet: less hops, less bandwidth

Free ride on always-on Gateways

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Energy issues

Variables– Network topology– Hardware power consumption– Placement algorithms– Content popularity– User behavior

Data available– DSL gateways and VoD servers power (Thomson)– Routers power (Cisco data)– Telefonica Spain and Peru network topologies– Imagenio VoD platform (Telefonica Spain)– Telefonica IPTV– Netflix movie popularity

αs = 4%

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VoD server power

αg = 1%

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Gateway power

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When would NaDa not work ?

# bytes transferred

power

Server

Gateways

Baselinepower

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When does NaDa work ?

# bytes transferred

power Server

Gateway

Baselinepower s

N

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Trace driven simulations

Traces from – Netflix, IPTV (Telefonica), YouTube

Content popularity from Netflix Topologies and workload from Telefonica Power numbers from Thomson’s gateway and IPTV

servers Popularity aware placement

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Simulation parameters

Gateway Storage 100MB-10GBGateway Upstream 0.1-2MbpsContent characteristics from data set Users 10k-30kContent window 10s-120sReplicas for warm content 1 (20s windows)Simulation duration 1 day - 86400 sRouter energy/bit 150 10−9

Server energy/bit 40 10−9

Gateway energy/bit 18 10−9

Power Usage Effectiveness (PUE) 1.7Home electricity cost factor 1.1Hops to server 4Hops to peer 2

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Total energy use (YouTube)

10k

20k

30k

40k

50k

60k

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Gateway storage

2500

3000

3500

4000

4500

5000

500 1,000 1,500 2,000 2,500 3,000

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Number of users

2500

3000

3500

4000

4500

5000

5500

15,000 20,000 25,000 30,000

Number of users

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Upstream bandwidth

3000

3500

4000

4500

5000

0.2 0.4 0.6 0.8 1.0 1.2

Video streaming >

uplink rateVideo streaming

< uplink rate

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Conclusions

Free-riding on existing infrastructure can significantly reduce load on conventional servers

Simulations demonstrated energy savings ranging from 20% to 60% versus data centers

Gateways can accomplish this with only modest resources (a few GB of storage, limited upload)

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Questions?

Potential QoS issues moving control from content providers (YouTube) to ISPs

Effects of consumer line overprovisioning

Security of content serving from home gateways