size scale matters: challenged networks...

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Size Scale Matters:Challenged Networks Everywhere?!

Jörg Ott

Technische Universität München

www.cm.in.tum.de

CHANTS Workshop

ACM MobiCom 2017

20 October 2017

2© 2017 Jörg Ott | Chair of Connected Mobility | TUM

CHANTS LandscapeWDTN / CHANTS

AOC

MobiOpp

ExtremeCom

DTN

RG

DTN

WG

Dagstuhl

2017

2015

2010

2005

2000

CC

SDS

SIGCOMM, MobiCom, MobiHoc, MobiSys, …INFOCOM, WoWMoM, PerCom, SECON, …

1998 (IPN)

2003

2013


Challenged Networks haven’t disappeared

(well, maybe some of the esoteric cases are now called ICN J)

they are just becoming more mainstream now

and often appear at different scales

• Flashback

• Scaling dimensions

• Case studies: Classical challenged networks

• When networks become challenged

• Perspectives


Roadmap


Flashback

SystemsApplications

Themes

Topics


Flashback

SystemsApplications

Themes

Topics

• Inter-planetary networking• Military networking• Maritime communications (AUVs, buoys, …)• Vehicular and UAV/drone ad-hoc networks • Sensor networks• Emergency and disaster communication• Networking for developing regions• Social opportunistic networking• Community and DIY networks


Flashback

SystemsApplications

Themes

Topics

• Micro blogging, twitter, e-mail• Web access and video streaming• Chat, blackboard, walkie-talkie, image sharing• Music (and video) sharing• Opportunistic Social Networks:

Safebook, D-Book, fbDTN• Distributing (multimedia) (education) content• Entertainment in crowds• Wildlife, environmental, and geo monitoring

(read: challenged IoT)• Medical monitoring, medical support


Flashback

SystemsApplications

Themes

Topics

• Generic platforms• ION• DTN2• IBR-DTN• SCAMPI + liberouter• k x Haggle• NetInf

• Integrated applications• Twimight• PodNet• Opphos• PirateBox & similar


Flashback

SystemsApplications

ThemesMobility data collection and analytics, mobility modeling – Message forwarding, scheduling, dropping, fragmentation – System and performance modeling – Message Ferrying and data mules –Routing^n – Network and source coding – Content distribution, content caching – MANET and IP-related topics – Efficient neighbor discovery – simulators – Implementation and performance evaluation – Convergence layers – Multi-contact encounters in dense networks – Mutex mechanisms, state synchronization – Security and Privacy –Context awareness – Social networking and communities; modeling, analysis, and exploitation – Data centric networks –Leveraging different radios – integrating cellular and Wi-Fi: Mobile offloading – Energy efficiency and power management –Incentive mechanisms – Resource awareness and management –Congestion control – Crowd computing – Sensor networks and crowd sourcing – Network and device monitoring and management – Time protocols – New flavors of transport or session protocols –(Centralized) control (channels) for content distribution – applications

Topics

• From theory to practice

• Sensor networks and applications

• Low energy systems

• Disaster scenarios and remote areas

• Internet-independent networks and applications

• Content sharing in different ways(point-to-point messaging disappearing)


Some trends

• Flashback




• Perspectives


Roadmap

(Selected) Scaling dimensions

© 2017 Jörg Ott | Chair of Connected Mobility | TUM 12

Node density

(Path) latency

Message size

Link performanceNetwork reach

Network size

Node / networkdynamics

Connectivity

• Defines: node degree/connectivity and interference• Implications: reachability, routing and resource management

• From energy efficient scanning for scarce contact opportunities• To node selection and congestion avoidance

• Research focus: towards sparse networks• Hybrid and adaptive routing schemes• Special considerations for dense networks


Node density

full meshsparse dense

• Defines: network stability• Implications: contact characteristics, node mixing

• Efficiency requirements for node discovery and data exchange• Routing algorithms and their optimization goals (e.g. spread vs. utility)

• Research focus: scenarios with at least partial motion• Fastest ones are probably the satellite ring road


Node / network dynamics

stationary fast movingmovingpartly moving


Connectivity (Kevin Almeroth, CHANTS 2007)

Continuousconnectivity

• Defines: availability of access to a network backbone• Also: connectivity among a set of nodes

• Example: duty cycling – always-on vs. sleepy sensor nodes

• Implications: instant reach and synchronization• Latency, “wormholes” through the backbone• Forwarding vs. carrying• Nature of the system and protocol design

• Research focus: usually mostly disconnected• From autonomous operation to intermittent (dis)connectivity

Intermittentdisconnectivity

Intermittentconnectivity

Infrequentconnectivity

Autonomousoperation


Link / path latency (+predictability)

ns weeks

• Defines: delivery delay (and control loop latency)• Implications: potential for interactivity and protocol design

• also a function of delivery reliability• from transaction-based protocols to self-contained messaging

• Research focus: tens of seconds to few days• Deterministic as in planetary and satellite schedules• Opportunistic as in pocket-switched networks

hours dayssecs mins


Link performance

bits/min Gb/s

• Considers bit error rate: effective data rate

• Defines: link and path capacity• Implications: delivery efficiency and protocol overhead

• Time needed to get something useful done• Feasibility of redundancy

• Research focus: kbit/s to Mbit/s• From sensor networks and low power nodes to WLAN-based systems• Hybrids for control vs. data channels

Mbit/skbits/s


Message size

Bytes GB

• Defines: feasible payload size• “What can a single message do.”• Part of a sequence vs. self-contained

• Implications: system operation and application design• Protocol design and APIs• Acceptable header overhead and per-message operations• Resource management• Transmission scheduling, multiplexing, and fragmentation

• Research focus: small “packets” to images and videos• From BLE beacons to image sharing to audio/video content delivery

MBKB


Network size

2 1010

• Defines: complexity, potential load, competition• Open vs. closed networks• Limiting to a relevant subset: Geographic area, time window

• Implications: how much a node can know / do about others• History of node contacts and associated information

• e.g. for utility computation• Space-time connectivity graph• Resource management (buffer multiplexing, subgrouping, priorities, …)• Trust and security models

• Research focus: 2 – 103, sometimes 104

103 106


Network reach

Local Global

• Defines: scope of operation• within vs. across social networks• closed vs. open groups

• Implications: feasible routing strategies, latency• flooding vs. opportunistic vs. deterministic routing• relayed or not• Also: feasible trust models

• Research focus: dual• local and neighborhood environments• global and inter-planetary

Inter-planetaryNeighborhood City …

The Internet


Node density

(Path) latency

Message size


Network size


Connectivity

• Flashback




• Perspectives


Roadmap

Case Study 1: Crowd Experience


Node density

(Path) latency

Message size


Network size


Connectivity


Opphos (KTH/SICS, 2013)

Sour

ce: h

ttps:

//ww

w.si

cs.s

e/pr

ojec

ts/o

ppho

s


Card-stunt as a Service (KAIST, 2017)

Video and paper available at: https://www.sigmobile.org/mobisys/2017/program.php

Case Study 2: DTN over Aerial Carriers


Node density

(Path) latency

Message size


Network size


Connectivity


Leveraging airplanes for bulk data [2009]

• Pick up from / delivery to an airport• Dijkstra routing


System model


Some findings (1): 100 messages


Some findings (2): Flight delays

Case Study 3: Here & Now


Node density

(Path) latency

Message size


Network size


Connectivity

• Experience sharingfor the moment

• Ephemeral socialnetwork of peoplenearby

• Content of localrelevance• in space and time

• Example• visiting a theme park


Here & Now

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Card-based representation in messages

NameContent hashApplication ID

Timestamp

Card idAuthor

Object data

Time-to-liveM

etad

ata

Self-containedobject data

Photo + annotation

Comment

Photo + annotation

Comment

Reply + photo

© 2017 Jörg Ott | Chair of Connected Mobility | TUM

Local anchor: Liberouter


To avoid fiddling with device-to-device ad-hoc networkingTo provide some notion of stable storageTo bootstrap mobile devices independent of an app store

Multiple interconnected to cover an area


Helsinki Slush 2014 Milano EXPO 2015


We tried this…

Two meta observations• Synchronization across many devices takes a long time• People don’t like bootstrapping apps to their own devices

• Naïve message forwarding has issues in dense networks• All nodes discover all others• Send messages repeatedly (unless already fully received by a peer)

• First step: reduce connectivity artificially


Part 1: Message synchronization

Full Mesh Limited Random Mesh Star Topology


Measurements (1)• Spreading k messages to 25 nodes• in a single WLAN

• Compose a star topology with the star node as a leader.• Instantiate a new leader and assign part of the clients to it.

• New leader creates a SoftAP / Wi-Fi Direct group.• Keep going recursively until all resources are used.

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Next step: Divide and Conquer

Star Topology Ch 1 Ch 6


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Next step: Divide and Conquer

ch 1

ch 6 ch 11

ch 36 ch 40 ch 44 ch 48

Leader Followers Wi-Fi network© 2017 Jörg Ott | Chair of Connected Mobility | TUM


Measurements (2)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

Frac

tion

Dis

trib

uted

Time [seconds]

Dissemination Process with 1, 2, and 3 Wi-Fi Channels, 50 MB content 3 channels 3 channels (sim) 2 channels 2 channel (sim) 1 channel

• Use the browser (, Luke)

• Extend the captive portal of the liberouter for content access• Web portal page structured according to applications

• Application-independent mechanisms to allow for innovation

• Mobile code to visualize and interact with arbitrary messages• Attached to each message as metadata

• (Use cookies and web storage for store-carry-forward)


Part 2: Enabling legacy devices

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A sample application object


Timestamp

Context reference…

Object data

Time-to-liveM

etad

ata

Self-containedobject data

Photo + annotation

Comment

Photo + annotation

Comment

Reply + photo


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Application functions: logic + data

App

Internet

f() {…}

Browser

Centralized data storeApplication logic

(backend)

Front-end logic + GUIClient data


Timestamp

Context reference

…

Object data

Time-to-live

Application logic (subset)


Localizing cloud functions (1)

ApplicationData Unit

(or Application)

new()

presentation()

reply()

summary()

+

HTML5

Name

Content hash

Application IDMetadata

Object data

Application logic (subset)


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Localizing cloud functions (2) d2 d1 c2 b4 b3 b2 a3 c1 a2 b1 a1 Content cache

a1

a2

a3

App a

b1

b2

b3

b4

App b

c2

c1

App c

d2

d1

App d

Grouping content by application identifier

Content summary view (message summary)

Per application view (application presenter)a1 c2 c1

d2

d1

a2 a3b1

b2

b3

b4

Composed web view

© 2017 Jörg Ott

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Localizing cloud functions (3)

Name

Content hash


Object data

summary()presentation()

reply()new()

Name

Content hash


Object data


reply()new()

Name

Content hash


Object data


reply()new()

Name

Content hash


Object data


reply()new()

Select content

Reply

Summarylayout H

TTP

Presentationlayout H

TTP

Composereply M

sg

Create newobject M

sg

© 2017 Jörg Ott

• Flashback




• Perspectives


Roadmap

What is making the Internet challenged?


Node density

(Path) latency

Message size

Link / path perf.Network reach

Network size


Connectivity

Example: Mobile Access Networks


Node density

(Path) latency

Message size


Network size


Connectivity


(Highly) Variable link performanceSource: Netradar.org

Performance = f(location, device, context, demand)= f(location, device, context,

time-of-day, day-of-week, season)


Sample streaming system

Streamingserver

Streamingclient

ControlLoop 1

NetworkCongestion Map

Service ControlLoop 2

Throughput updatesLook-ahead requests

ExpectedThroughput

prediction


Performance prediction and adaptation

t (in sec)!

BW (i

n kb

ps)!

Playback Rate!

Time To Outage (Ttto)!

Channel Capacity!

Receiver Rate!4. Data pre-buffered due to predictive feedback!

Initial !pre-buffering!

Time Duration of Outage (Tgap)!

2.Congestion hole detected! 3. Client schedules

variable transmission rate or switches to lower rate!

1. Look-ahead for holes!

5. Client resets to normal media rate or transmission rate!

Example: Internet for Remote Regions


Node density

(Path) latency

Message size


Network size


Connectivity


Remote Internet Access

Internet

Fronthaulnetwork

$$$$

• Low capacity• Less than best effort

(scavenging service)• Implied high latency• Extensive caching• Possibly curated content• Local surrogate services• Time-shifted delivery• Request bundling

Example: IoT


Node density

(Path) latency

Message size

Link / path perf.Network reach

Network size


Connectivity

Example: Visible Light Services


Node density

(Path) latency

Message size


Network size


Connectivity

• Flashback




• Perspectives


Roadmap

• Long history with themes to last• Esoteric use cases didn’t succeed

• Many lessons learned to be applied elsewhere• Extreme b/w x delay product• Offline operation• Robustness features

• Current Internet trends:Challenged networks vs. networking challenges?• Cloudification of (mobile) apps and services• Ubiquity of IoT• Expanding the (mobile) network in performance and reach


Some concluding thoughts

SCAMPI

• Teemu Kärkkäinen, Mika Välimaa, Esa Hyytiä, Jörg Ott: Opportunistic Content Dissemination Performance in Dense Network Segments. Proceedings of the ACM MobiCom workshop on Challenged Networks (CHANTS), October 2016.

• Teemu Kärkkäinen, Jörg Ott, Lorenzo Valerio, Paul Houghton, Andrea Passarella: Here & Now: Data-centric Local Social Interactions through Opportunistic Networks. (demo paper) Proceedings of the ACM MobiCom workshop on Challenged Networks (CHANTS), October 2016.

• Teemu Kärkkäinen, Jörg Ott: Liberouter: Towards Autonomous Neighborhood Networking. Proceedings of IEEE/IFIP WONS, March 2014.

• Marcin Nagy, Teemu Kärkkäinen, Jörg Ott: Enhancing Opportunistic Networks with Legacy Nodes. ACM SIGMOBILE Mobile Computing and Communications Review: Volume 18 Issue 3, July 2014.

• Mikko Pitkänen, Teemu Kärkkäinen, Jörg Ott, Marco Conti, Andrea Passarella, Silvia Giordano, Daniele Puccinelli, Franck Legendre, Sacha Trifunovic, Karin Hummel, Martin May, Nidhi Hegde, Thrasyvoulos Spyropoulos: SCAMPI: Service Platform for Social Aware Mobile and Pervasive Computing.ACM SIGCOMM Computer Communication Review, Volume 42 Issue 4, pp 503-508, September 2012.

• Ari Keränen, Jörg Ott: DTN over Aerial Carriers. Proceedings of the 4th ACM MobiCom Workshop on Challenged Networks, Beijing, China, September 2009.


Some references

size scale matters: challenged networks...

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