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Access Networks: Applications and Policy. Nick Feamster CS 6250 Fall 2011. (HomeOS slides from Ratul Mahajan). Huge amount of tech in homes. Home users struggle. Management Nightmare. Integration Hurdles. Why developers are not helping. Vendors only build islands.

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Access networks applications and policy

Access Networks:Applications and Policy

Nick FeamsterCS 6250Fall 2011

(HomeOS slides from Ratul Mahajan)



Home users struggle
Home users struggle

  • Management Nightmare

  • Integration Hurdles



Vendors only build islands
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


Interoperability is not sufficient
Interoperability is not sufficient

  • Media: DLNA, AirTunes, etc.

  • Devices: UPnP, SpeakEasy, mDNS, etc.

  • Home Auto: ZwaveZigBee, X10, etc.

Video Recording

Climate

Control

Camera-Based Entry

Remote Lock


Monolithic systems are inextensible
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
An alternative approach: A home-wide operating system

HomeStore

Video Rec.

Remote Unlock

Climate

Operating System


Goals of homeos
Goals of HomeOS

  • Simplify application development

  • Enable innovation and device differentiation

  • Simplify user management


Simplify development
Simplify development

App A

App B


Simplify development1
Simplify development

App A

App B

Mgmt

UI

Access Control

Port

Port

Driver

Driver


Roles in homeos
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
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
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
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
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
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
Detecting Network Neutrality Violations with Causal Inference

Mukarram Bin Tariq, Murtaza Motiwala

Nick Feamster, Mostafa Ammar

Georgia Techhttp://gtnoise.net/nano/


The network neutrality debate
The Network Neutrality Debate Inference

Users have little choice of access networks.

ISPs want to “share” from monetizable traffic that they carry for content providers.

November 6, 2006


Goal make isp behavior transparent
Goal: Make ISP Behavior Transparent Inference

Source: Glasnost project

Our goal: Transparency.Expose performance discrimination to users.


Existing techniques are too specific
Existing Techniques are Too Specific Inference

  • 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


Main idea detect discrimination from passively collected data
Main Idea: Detect Discrimination From Passively Collected Data

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

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


Ideal directly estimate causal effect
Ideal: Directly Estimate Causal Effect Data

Causal Effect= E(Real Throughput using ISP)

E(Real Throughput not using ISP)

Performance with the ISP

Baseline Performance

“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.


Instead estimate association from observed data
Instead: Estimate Association from DataObserved Data

Observed Performance with the ISP

Association= E(Observed Throughput using ISP)

E ( Observed Throughput not using ISP)

Observed Baseline Performance

Problem: Association does not equal causal effect.

How to estimate causal effect from association?


Association is not causal effect
Association is Not Causal Effect Data

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.

ClientSetup

Time

of

Day

Comcast

?

Location

Content

BTThroughput


Strawman random treatment
Strawman: Random Treatment Data

Common approach in epidemiology.

H

S

S

S

H

S

S

S

H

Untreated

Treated

H

H

H

H

S

H

S

S

S

= 0.8 - 0.25 = 0.55

α  θ

S = “sick”H = “healthy”

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.


The internet does not permit random treatment
The Internet Does Not Permit Random Treatment Data

Alternate approach: Stratification

  • 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)


Stratification adjusting for confounders
Stratification: Adjusting for Confounders Data

Causal Effect (θ)

0.55

-0.11

Strata

H

H

H

H

H

Treated

H

H

H

H

H

H

H

H

S

S

S

S

S

S

S

S

0.75

0.44

H

S

Baseline

H

H

H

H

H

S

S

S

S

S

S

S

0.20

0.55

Step 1:Enumerate confounderse.g., setup ={ , }

Step 2:Stratify along confounder variable values and measure association

Association implies causation (no otherexplanation)


Stratification on the internet challenges
Stratification on the Internet: Challenges Data

What is baseline performance?

What are the confounding variables?

Which data to use, and how to collect it?

How to infer the discrimination method?


What is the baseline performance
What is the baseline performance? Data

  • Baseline: Service performance when ISP not used

    • Need some ISP for comparison

  • Approach: Average performance over other ISPs

  • Limitation: Other ISPs may also discriminate


What are the confounding variables
What are the confounding variables? Data

  • 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


What data to use how to collect it
What data to use; how to collect it? Data

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

  • 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


Evaluation three experiments
Evaluation: Three Experiments Data

Experiment 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


Experiment setup
Experiment Setup Data

Clients Running NANO-Agent

D1

D2

N1

N2

N3

Internet

ISPs

Access ISP

5 ISPs in Emulab

2 Discriminating

Service Providers

PlanetLab nodes

HTTP and BitTorrent

Discrimination

Throttling and dropping

Policy with Click router

Confounding Variables

Server location

near servers (West coast nodes)

far servers (remaining PlanetLabnodes)

~200 PlanetLab nodes


Without stratification detecting discrimination is difficult
Without Stratification, Detecting Discrimination is Difficult

Simple Discrimination

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


Stratification identifies discrimination
Stratification Identifies Discrimination Difficult

Discriminating ISPs have clearly identifiable causal effect on throughput

Simple

Long-Flow

BitTorrent

Neutral ISPs are absolved


Implementation and deployment
Implementation and Deployment Difficult

http://gtnoise.net/nano/

Performance

Relative to Other Users

DNS

Latency

Traffic

Breakdown

Throughput

  • Implementation

    • Linux version available

    • Windows and MacOS versions in progress

  • Now: 27 users

    • Need thousands for inference

  • Performance dashboard may help attract users


Summary and next steps
Summary and Next Steps Difficult

  • 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/


Nano can infer discrimination criteria
NANO Can Infer Discrimination Criteria Difficult

Approach

Evaluation

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

cum_pkts <= 10103 -> not_discriminated

cum_pkts > 10103 -> discriminated



Why association causal effect
Why Association != Causal Effect? Difficult

Sleep

Aspirin

Diet

?

Health

Age

OtherDrugs

  • 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


Causality an analogy from health
Causality: An Analogy from Health Difficult

Epidemiology: study causal relationships between risk factors and health outcome

NANO: infer causal relationship between ISP and service performance degradation


Without stratification detecting discrimination is hard
Without Stratification, Detecting Discrimination is Hard Difficult

Simple Discrimination Experiment

Long Flow Discrimination Experiment

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


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