Future Internet and intelligent networks

6.3.2012 TIES410 - Future Internet, University of JyväskyläJyrki HuuskoVTT Technical Research Centre of Finland

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WHAT IS THE FUTURE INTERNET ABOUT?

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IT IS ABOUT ALL OF THIS AND EVEN MORE…

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The Vision

Global communication network which is qualitatively better than the current Internet by taking into consideration the constraints of current Internet, wireless and wired convergence and service requirements

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“In ‘90s Manuel Castells and Jan van Dijk specified the term ‘Network Society’ as a society where the key structures are organized around information networks. The ‘Network Society’ can be seen as an enhancement of the information society. In this case, it is not only the technology that defines the behaviour of society, but also political, cultural and economical factors together with information society’s technological advances build up the network society. It can be also seen that different social and media networks itself shape the organizational forms and the most important infrastructures of the society.” [1]

[1] J.Huusko in P. Ruuska et al., “ROADMAP for Communication Technologies,, Services and Business Models 2010, 2015 and Beyond,” Tekes GIGA programme.

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Inte

rnet

for p

eopl

e In

tern

et b

y pe

ople

Inte

rnet

of M

edia

(IoM

)

Inte

rnet

of T

hing

s (Io

T)R

eal-w

orld

Inte

rnet

Inte

rnet

of S

ervi

ces (

IoS)

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DO WE REALLY NEED A TOTALLY NEW INTERNET FOR ALL OF THIS?

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• From the network point of view, the Internet has not changed much butthe way how it is used has changed

• The main task for the network engineers would be to construct the solidand robust ground for the new use methods of the Internet

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Changes in Internet traffic characteristics Increasing amount of video and audio content by both professionalcontent creators and usersSocial networks will feed the content creation and sharing increasingtraffic load not only in fixed network domain but also in mobile domainIn addition for HDTV and 3DTV the new Ultra High Definition Television of ’20s will create demands for network and service technologies in terms of e.g. high bandwidth, and robustnessSmall sensor/actuator devices also as source of contentMore and more mobile users and devices

EC Digital Agenda: changes also in business modelsSimplified copyright clearance, management and cross-border licensing

possibilities for new players

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The content delivery is one of the main services causing the bottlenecks in network

Sharing, storing and accessing different information and content at home and on the moveNext generation UHDTV content for home cinemas

In addition to media content, the private and public cloud services and data preservation demand massive data reserves data centresEnergy consumption is a key issue service and communication systemsas well as data centres need to be optimized for sustainable resourceutilizationOperational expenses (OPEX) for network and service management areincreasing due to the complexity of network infrastructures autonomicsolutions in network management and the efficient ways to balance load areneeded

It is not only the technical problems which needs to be addressed, the regulative and legislative problems need to be solved too

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Some key challenges for the future network architectureApplying new business models and enabling new entrepreneurshipEfficient solutions for network and service management Bypassing the bottlenecks such as network congestions and bandwidthutilization problems for new servicesOptimizing the network and service infrastructure towards energy-efficient solutionsIntelligent arrangement of data reserves, storages and cachesTrusted and secure content access and deliveryIntegrating sensor/actuator ”things” to Internet

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Developments and changes

Future vision Challenges and gaps Future solutions and research needs

socialmedia

User gen. content

energy-awareness

mobility withgood quality of

experience

data reserves

security and trust

intelligent caching, storaging

system management and scalability

changing business models

service andnetwork

managementnetworked

object security and trust

system intelligence and

cognition

business models

complex infrastructures

small devices

energy efficiency

signalling andtransport

ensureQoE

next gen. TV, entertainment

bandwidthusage

Augmentedreality

Content andinformation

centric world

J.Huusko&J.Ahola, FIA research roadmap, 2011

1308/03/2012

The Internet architecture is starting to reach the limits of its remarkable ability to adapt and would need a fundamental overhaul:

• Growth in traffic volume and interconnect devices, • Impact of video and multimedia distribution, • Energy consumption, • Complexity and operational costs (OPEX),• Security, privacy and trust

One of the current trends to overcome at least some of the problems is to go towards

perceivable, intelligent, self-managing autonomous networks

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?

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There is currently no alternative design models for Internet which has a better cost/performance ratio compared to the current Internet designRevolutionary ”clean-slate” approaches are though needed to create new alternative solutions and to feed the incremental development with new ideas

Interaction needed

1608/03/2012

Information-Centric Networking

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Internet has reached the point where trustworthy service provisioning and network management is challenging

Major part of Internet traffic is generated by content distribution

User’s expectations of network connection: wherever and whenever even with high mobility…and in reliable way

In initial stage of the Internet, the main consideration was definition of end-host location – nowadays the information itself has become more important

New work with content- or information-centric networking paradigm (NetInf, CCN, PSIRP, …)

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The Network of Information (NetInf) relies on information-centric networking approach

main consideration is the information itself, not end-host location.

One of the main characteristics is a secure naming and object/information identifying solution

One of the main applications for NetInf is the content distribution and personal mobile communications

A support for multi-access environment is also considered in order to improve the content access and network load balancing

NetInf based solutions can also improve energy efficiency and with secure naming solution it can provide also a more “controlled” content distribution from content provider point of view

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Traditional content accessI’ll need

object A from the server B

Secure connection established to specific

trusted server

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Object access with NetInf

I wantobject B

Untrusted connection

Untrustedpeers/servers/

networkedelements

Trusted copy of object B

o Multi-access (alsosimultaneous) capabilities to improve the object accesso Possibilities to prioritizeacess to localized information

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Intelligent Networks

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Intelligent network = Cognitive Network

Autonomic, self-modifying network with learning capabilities

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Simple definition of cognitive network:

“A cognitive network has a cognitive process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals.” [2]

Monitoring and observing the environment: utilizing e.g. probes, agentsand measuring the network key performance indicators (KPIs) etc.

perceive current network conditions plan decide act

Planning/orientation: analysis of the situation and e.g. policies affecting to decision making, impact of previous decisions and acts, generates and evaluates the alternatives,… Decision making: the actual decision making/hypothesis procedure. It canbe also tightly coupled with orientation and actions. Implementation of decision making algoritms based on e.g. fuzzy logics, game theory, geneticalgorithms, expert systems,… Execution/action: Execution of the system modifications and directinteraction with environment based on the decision making.

[2] Thomas, R.W.; DaSilva, L.A.; MacKenzie, A.B.; , "Cognitive networks,” 2005

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The cognitive process in cognitivenetwork is principle an OODA loop(Observe, Orient, Decide and Act)Cognitive Radio (CR) can beconsidered as a special case of the Cognitive Network (CN)

The CR implements the cognitive process mainly at OSI layers 1 and 2, while the CN considers the wholesystem

Network system may implementseveral OODA loops in differentsystem levelsIn order to fully achive end-to-end goals, the OODA loops need to be executed in co-operativemanner

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…and all this implies more and more cross-layering and control/management signalling…

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…Smart handovers, distributed decision making and fuzzyreasoning…

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Considering an example case of smart handoversThe handover performancedepends on the variety of different factors in heterogeneous environmentThe decision making procedurecan benefit from distributedcontrol and management bytaking into account

The conditions at bothterminal and network sideDifferent networkcharacteristics DCMF architecture overview

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Hierarchical/co-operativedecision making at different levels and layersNetwork decision can beonly tips for service and terminal driven decisionsCross-layer informationcollection can beenhanced with standardized informationservice solutionsIntegration of networkmanagement and higherlevel service ontologies

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Support different access networks, Data collection using probesNetwork and client side expert system supportEvent/trigger signalling inside the network and between network and terminal

Observation and monitoring

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Network information services supporting decision makingExample – MIH Information Service

Static information about network capabilites and point of attachments (PoA) in rangeExpanding service also to dynamic information such as current traffic loadsin access pointsNew extensions could be considered for cognitive decision making e.g. howto incorporate the service/application specific ontologies for IoTnetwork/gateway management and configuration

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Network Expert System for intelligent handovers

Network Expert System for network side intelligenceLearning capabilities basedon Self-Organizing Maps(SOM, Kohonen Maps)Probes implemented for WiFi, 3G, WiMAXProbes collect the networktraffic information which is further processed in principal component analysis (PCA)The collected data is further clusterized basedon its characteristics and specific triggers aregenerated for event trigger

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Distributed Decision Engine – Overview

Distributed information monitoring, decision making and message distribution framework for cognitive resource management

Publish/Subscribe type of message (event) delivery

Common interface for collecting information, making decisions and delivering information/decisions to “user”

Interactions within network domain but also between different networksInteraction/information collection between/from various types of devices and technologies, including: access points, switches, routers,…

Common interface enables integration with other systems and modules

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Utilizing the standardized management interfaces (e.g. SNMP, IEEE 802.21 MIH, OMA DM etc.) Supports for Hierarchical/Cascading decision makingSupport for policy based management

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Example: fuzzy logic based network selection algorithm”Simple and lightweight” reasoning methodThe example is based on Mamdani-type cascade fuzzy interface system (FIS)

Fuzzification: transforms crisp input values to fuzzy set. In Mamdani-type FIS ”min-max” operations (AND/OR –operations) are used in fuzzification and evaluation of antecedent rulesInference Engine: generates the consequent fuzzy set using the if-then rulesand e.g. in Mamdani FIS aggrecates the fuzzy setsDefuzzification: transforms the new fuzzy set to crisp output valueKnowledge Base: stores the fuzzy logic if-then rules

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If Users is LOW and % Packet loss is LOW then AP Load is VERY_GOOD

If Users is HIGH or % Packet loss is HIGH then AP Load is VERY_BAD

If Users is MOD. and Available Bandwidth is MOD. then AP Load is REGULAR

Users = 8 % Packet Loss = 5

Available Bandwidth =

45

0

0

0

24

24

24

0 100

0 100

0 100

0 100

0 100

0 100

INPUTS

1.Fuzzify inputs 2.Apply fuzzy operation(AND =min OR=max)

3.Apply modulation by clipping

4.Apply aggregation

method (max)

5.Defuzzify (Centre of Gravity)

OUTPUT

AP Load =43%

0

0

0

0

100

100

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100

3808/03/2012

The Mamdani fuzzy interface is usefull for adequately low number of variableThe number of rules increase exponentially with the number of input variablesIf the number of variables is high it is hard to find the relationshipbetween the antecendent rules and consequents

In order to efficiently use the fuzzy logics it is important to find the correct set of input parameters for the problem and limit the set to only the necessaryones

There are other FIS solution such as Sugeno’s method which could be usefulfor larger fuzzy sets

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The following video demonstrates some solutions for intelligent handovers presented in previous slides

http://www.youtube.com/watch?v=_AKqV8m75Ws

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…Self-organizing Wireless Mesh Network (WMN) for cellular backhauling…

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Aggregation network

WMNGateway node

WMN Gateway node

~ 5…

10 k

m

~ 5…10 km

X

each WMN noderepresents a base station MWR or FSO

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In the directional WMN concept, a set of client nodes (base stations) areconnected together with point-to-point wireless links in a partial meshtopology.

This group of nodes forms a WMN cloud, a self-organized routing area withdynamic traffic engineering capabilities.

With the in-built QoS aware routing and load balancing capability the WMNprovides

automatic and transparent resiliency against failures

flexible use of available transport capacity in time-varying traffic loadsituations

guaranteed minimum capacity for high priority traffic even in extreme rainconditions.

WMN - basic features

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The WMN mesh topology can be adapted to different base station coverage scenarios: hierarchical and interleaved WMN clouds are supported to cover a wider geographical area and to support different transport needs.

Typical size of the cloud is 20 – 30 base station sites, but up to 200 client nodes can be supported. The covered geographical area depends on the chosen link technology: wireless, FSO, even wireline Ethernet, fibre or any mix of those are possible.

Use of millimeterwave PtP radios allows very high capacity connections through the cloud and electrically steerable beam antennas enable easy and low cost deployment e.g. to walls, lamp posts, poles, etc.

Each WMN cloud is connected to the external transport network via one or more WMN gateways.

WMN topology

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Self Configuration: Flexible network deployment and operation via mesh SON and steerable antennas

Automated configuration and installationBlocked line-of sight can be circumvented with mesh network design (virtual NLOS) On-line topology change to cope with temporary obstructions on the radio path and equipment failures, without need for quick repair (reduced mean-time-to-repair “MTTR” and OPEX)

Self Healing: High network level reliability and resiliency under varying environmental conditions

Selection among multiple available routes in the mesh topology to recover from link faults, LOS blocking, gradual link degradation due to rain, etc.Hitless fast protection switching (few us vs. traditional 50 ms) QoS-aware operation: guaranteed service for high priority traffic

Self Optimization: Transparent and automatic operation to optimize the use of available transport capacity by demand

Dynamic route optimization and load balancing keeps cloud throughput optimized in varying traffic load conditions

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…then there is the Cognitive Radio…

4608/03/2012

…but that is another bedtime story…

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Something to read

Joseph Mitola III, ”Cognitive Radio - An Integrated Agent Architecture for Software Defined Radio”,2000Ryan W. Thomas, Luiz A. DaSilva, Allen B. MacKenzie, "Cognitivenetworks”, 2005Future Internet – The Cross-European Technology Platform Vision Document, 2009Pekka Ruuska, Jukka Mäkelä, Marko Jurvansuu, Jyrki Huusko and Petteri Mannersalo, ”Roadmap for communication technologies, services and business models 2010, 2015 and beyond”, Tekes, 2010Future Internet Assembly, ”FIA Research Roadmap”, 2011

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To boldly go where no engineer has gone before

4908/03/2012

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