Access Networks:Applications and Policy

Nick FeamsterCS 6250Fall 2011

(HomeOS slides from Ratul Mahajan)

Huge amount of tech in homes

Home users struggle

• Management Nightmare

• Integration Hurdles

Why developers are not helping

Application

HardwareThe actual devices in the house

Application

TopologyHandle WiFi vs. 3G vs. Eth, Subnets

HardwareThe actual devices in the house

Application

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth, Subnets

HardwareThe actual devices in the house

Application

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth, Subnets

HardwareThe actual devices in the house

Application

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth, Subnets

HardwareThe actual devices in the house

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth, Subnets

HardwareThe actual devices in the house

Vendors only build islands

• Vertically integrate hardware and software

• Seldom make use of other vendors’ devices

• No single vendor comes close to providing all the devices a home needs

ClimateControl

Remote Lock

Camera-Based Entry

Video Recording

Interoperability is not sufficient

• Media: DLNA, AirTunes, etc.• Devices: UPnP, SpeakEasy, mDNS, etc.• Home Auto: Zwave ZigBee, X10, etc.

Monolithic systems are inextensible

• Security: ADT, Brinks, etc.• Academic: EasyLiving, House_n, etc.• Commercial: Control4, Elk M1, Leviton, etc.

Home Media

Security

An alternative approach: A home-wide operating system

Operating System

Video Rec.

Remote Unlock

Climate

HomeStoreHomeStore

Goals of HomeOS

• Simplify application development

• Enable innovation and device differentiation

• Simplify user management

Simplify development

…

…App

AApp

B

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth,

Subnets

HardwareThe actual devices in the house

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyHandle WiFi vs. 3G vs. Eth,

Subnets

HardwareThe actual devices in the house

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceHandle different brands/models

TopologyLogically centralize devices

HardwareThe actual devices in the house

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationWhen apps disagree, who wins?

DeviceStandardize at functional layer

TopologyLogically centralize devices

HardwareThe actual devices in the house

Application Logic

User PreferenceWhat is automated? When? How?

CoordinationAccess control mediates conflicts

DeviceStandardize at functional layer

TopologyLogically centralize devices

HardwareThe actual devices in the house

Application Logic

User PreferenceUsers’ manage access control rules

CoordinationAccess control mediates conflicts

DeviceStandardize at functional layer

TopologyLogically centralize devices

HardwareThe actual devices in the house

Simplify development

…

…App

AApp

B

DriverDriver DriverDriver…PortPort PortPort

…

Access Control

MgmtUI

Roles in HomeOS

• Roles are functional descriptions of ports– lightswitch, television, display, speakers, etc.– App developers program against roles

• Enable vendors to innovate/differentiate– Anyone can create a new role

• e.g., SonyBraviaTV vs. television• Allows new functionality to be rapidly exposed

– Commodity vendors can still participate

Simplify user management

• Conducted a field study– Modern homes with automation & other tech– 14 homes, 31 people

• Users’ needs for access control– Applications as security principals– Time in access control decisions– Confidence in their configuration

Management primitives

• Datalog access control rules– (port, group, module, time-start, time-end, day, priority,

access-mode)– Reliable reverse perspectives help users confidently

configure access control

• User accounts– Can be restricted by time (guests)

• Application manifests– Specify role requirements for compatibility testing– Simplifies rule setup (only when roles match)

Implementation status

• Built on the .NET CLR• ~15,000 lines of C#

– ~2,500 kernel

• 11 Applications– Average ~300 lines/app

• Music Follows the Lights– Play, pause & transfer music

where lights are on/off

• Two-factor Authentication– Based on spoken password

and face recognition

Open questions/Ongoing work

• Additional evaluation– Is it easy to write apps and drivers?– Is it easy to manage?– Does it scale to large homes?

• Deploy & support application development

• Explore business/economic issues

Summary

• A home-wide OS can make home technology manageable and programmable

• HomeOS balances stakeholder desires– Developers: abstracts four sources of heterogeneity– Vendors: enables innovation and differentiation– Users: provides mgmt. primitives match mental models

http://research.microsoft.com/homeos

Detecting Network Neutrality Violations with Causal Inference

Mukarram Bin Tariq, Murtaza MotiwalaNick Feamster, Mostafa Ammar

Georgia Tech

http://gtnoise.net/nano/

19

November 6, 2006

The Network Neutrality DebateUsers have little choice of access networks.ISPs want to “share” from monetizable traffic that they carry for content providers.

20

Goal: Make ISP Behavior Transparent

Our goal: Transparency.Expose performance discrimination to users.

Source: Glasnost project

21

Existing Techniques are Too Specific

• Detect specific discrimination methods and policies– Testing for TCP RST packets (Glasnost) – ToS-bits based de-prioritization (NetPolice)

• Limitations– Brittle: discrimination methods may evolve– Evadable

• ISP can whitelist certain servers, destinations, etc.• ISP can prioritize monitoring probes• Active probes may not reflect user performance• Monitoring is not continuous

22

Main Idea: Detect Discrimination From Passively Collected Data

• Objective: Establish whether observed degradation in performance is caused by ISP

• Method: Passively collect performance data and analyze the extent to which an ISP causes this degradation

This talk: Design, implementation, evaluation, and deployment of NANO

23

Ideal: Directly Estimate Causal Effect

Baseline Performance

Performance with the ISP Causal Effect = E(Real Throughput using ISP) E(Real Throughput not using ISP)

“Ground truth” values for performance with and without the ISP (“treatment variable”)

Problem: Need both ground truth values observed for same client. These values are typically not available.

24

Association = E(Observed Throughput using ISP)

E ( Observed Throughput not using ISP)

Instead: Estimate Association from Observed Data

Observed Baseline Performance

Observed Performance with the ISP

Problem: Association does not equal causal effect.How to estimate causal effect from association?

25

Association is Not Causal Effect

ComcastComcast OtherOtherISPsISPs

Avg. Avg. BitTorrentBitTorrent

ThroughputThroughput

5 kbps

10 kbps

ComcasComcastt

BTBTThroughputThroughput

?

ClientClientSetupSetup

TimeTimeofofDayDay

ContentContentLocationLocation

Why? Confounding variablescan confuse inference.

• Suppose Comcast users observe lower BitTorrent throughput.

• Can we assume that Comcast is discriminating?

• No! Other factors (“confounders”) may correlate with both the choice of ISP and the output variable.

26

Strawman: Random Treatment

• Treat subjects randomly, irrespective of their initial health.

• Measure association with new outcome.

• Association converges to causal effect if the confounding variables do not change during treatment.

= 0.8 - 0.25 = 0.55

Treated

H H H

H S

Untreated

H

S S

S

S

H H

HSS

S S S

α θ

Common approach in epidemiology.

S = “sick”H = “healthy”

27

The Internet Does Not Permit Random Treatment

• Random treatment requires changing ISP.

• Problems– Cumbersome: Nearly impossible to achieve for large

number of users– Does not eliminate all confounding variables (e.g.,

change of equipment at user’s home network)

Alternate approach: Stratification

28

Stratification: Adjusting for Confounders• Step 1: Enumerate

confounderse.g., setup ={ , }

• Step 2: Stratify along confounder variable values and measure association

• Association implies causation (no otherexplanation)

H H HH H H

H H H

S S S

H SS S S

H HH HS SS S

S

H HH H HS SS S

0.75 0.44

0.20 0.55

Strata

0.55 -0.11Causal Effect (θ)

29

Stratification on the Internet: Challenges

• What is baseline performance?

• What are the confounding variables?

• Which data to use, and how to collect it?

• How to infer the discrimination method?

30

What is the baseline performance?

• Baseline: Service performance when ISP not used– Need some ISP for comparison

• Approach: Average performance over other ISPs

• Limitation: Other ISPs may also discriminate

31

What are the confounding variables?

• Client-side– Client setup: Network Setup, ISP contract– Application: Browser, BT Client, VoIP client– Resources: Memory, CPU, network utilization– Other: Location, number of users sharing home

connection

• Temporal– Diurnal cycles, transient failures

32

What data to use; how to collect it?

• NANO-Agent: Client-side, passive collection – per-flow statistics: throughput, jitter, loss, RST packets– application associated with flow– resource monitoring

• CPU, memory, network utilization

• Performance statistics sent to NANO-Server– Monitoring, stratification, inference

http://www.gtnoise.net/nano/

33

Evaluation: Three ExperimentsExperiment 1: Simple Discrimination

– HTTP Web service– Discriminating ISPs drop packets

Experiment 2: Long Flow Discrimination– Two HTTP servers S1 and S2

– Discriminating ISPs throttle traffic for S1 or S2 if the transfer exceeds certain threshold

Experiment 3: BitTorrent Discrimination– Discriminating ISP maintains list of preferred peers – Higher drop rate for BitTorrent traffic to non-preferred

peers

34

Experiment SetupAccess ISP

5 ISPs in Emulab

2 Discriminating

Service ProvidersPlanetLab nodes

HTTP and BitTorrent

DiscriminationThrottling and dropping

Policy with Click router

Confounding VariablesServer location

near servers (West coast nodes)

far servers (remaining PlanetLab nodes)

Internet

D1 D2 N1 N2 N3

~200 PlanetLab nodes

ISPs

Clients Running NANO-Agent

35

Without Stratification, Detecting Discrimination is Difficult

Overall throughput distribution in discriminating and non-discriminating ISPs is similar.

Simple Discrimination

36

Stratification Identifies Discrimination

Discriminating ISPs have clearly identifiable causal

effect on throughput

Neutral ISPs are absolved

Simple Long-Flow BitTorrent

37

Implementation and Deployment

• Implementation– Linux version available– Windows and MacOS versions in progress

• Now: 27 users– Need thousands for inference

• Performance dashboard may help attract users

Throughput DNSLatency

TrafficBreakdown

PerformanceRelative to Other Users

http://gtnoise.net/nano/

38

Summary and Next Steps

• Internet Service Providers discriminate against classes of users and application traffic today.

• Need passive approach– ISP discrimination techniques can evolve, or may not be

known to users.– Tradeoff: Must be able to enumerate confounders

• NANO: Network Access Neutrality Observatory– Infers discrimination from passively collected data– Detection succeeds in controlled environments– Deployment in progress. Need more users.

http://gtnoise.net/nano/

39

40

NANO Can Infer Discrimination Criteria

ISP throttles throughput of a flow larger than 13MB or about 10K packets

cum_pkts <= 10103 -> not_discriminatedcum_pkts > 10103 -> discriminated

EvaluationApproach

41

Sufficiency of Confounding Variables

42

Why Association != Causal Effect?

• Positive correlation in health and treatment

• Can we say that Aspirincauses better health?

• Confounding Variables correlate with both cause and outcome variables and confuse the causal inference

AspirinAspirin No No AspirinAspirin

HealthyHealthy 40% 15%

Not Not HealthyHealthy 10% 35%

AspirinAspirin

HealtHealthh

?

SleepSleep DietDiet

OtherOtherDrugsDrugsAgeAge

46

Causality: An Analogy from Health

• Epidemiology: study causal relationships between risk factors and health outcome

• NANO: infer causal relationship between ISP and service performance degradation

47

Without Stratification, Detecting Discrimination is Hard

Overall throughput distribution in discriminating and non-discriminating ISPs is similar.

Server location is confounding.

Simple Discrimination

Experiment

Long Flow Discrimination

Experiment