Future Internet Tutorial - Requirements and Challenges - IWT 2011

Future Internet: New Network
Architectures and Technologies

Part III - Requirements & Challenges
Antônio Marcos Alberti

© Antônio M. Alberti 2011

Outline
1. Substrate Resources and Its Integration with Software
2. Information, ID/Loc Splitting, Semantic, Context and Mobility
3. Autonomic and Cognitive Technologies
4. Security, Privacy and Trust
5. Services and Applications
6. Simplicity, Sustainability and Evolvability
7. Artificial Intelligence and Other Bio-inspired ICT


1. Substrate Resources and Its Integration with Software
 Technology Evolution
 Capacity and Ubiquity
 Internet of Things
 Real World Internet
 Virtualization


Technology Evolution
 Moore's Law:
 Predicts technological developments in computing power.

 (Kurzweil, 2005):
 A theory for technological evolution – to describe the exponential
growth of technological advances: The Law of Accelerated Returns


Technology Evolution
 The Law of Accelerating Returns: Two positive feedback loops
1. Selection of the more capable techniques of a certain stage to
build the next stage – increases the rate of progress
exponentially, reducing required time to obtain the same results.

2. Selected process becomes more attractive than others and
begins to catalyze resources to it – starts to evolve even faster,
experiencing an additional exponential growth over the 1st.

Source: http://www.kurzweilai.net/the-law-of-accelerating-returns


Capacity and Ubiquity
 Exponential growth trends for:
 Memory capacity (DRAM in bits per dollar), microprocessor clock speed
(Hz), transistors per chip, processor performance (MIPS), magnetic
storage (bits per dollar), the number of hosts on the Internet.

 (Saracco, 2009):
 Consistent technological developments in:
 Computing – is achieving teraflops right now and evolution proceeds to
petaflops in the next decade.
 Display technology – has advanced enormously in later years.
 Consumer electronics, such as handsets, laptops, HDTVs, e-books,
video games, GPSs, etc.


 Minnesota Internet Traffic Studies (MINTS):
 Annual Internet traffic growth rates were about 50-60% in 2008
and about 40-50% in 2009.
 The monthly Internet traffic was circa 7.5-12x1018 bytes or
exabytes.

 Japanese Akari project:
 Traffic could increase 1.7 times per year in Japan in the next
years, producing an expansion of 1000 times in 13 years.

 We can expect a growth of roughly 30-100 times in the next
decade.


 How to meet this demand?
 Mobile Access: 4G, Cognitive Radio (CR).

 Fixed Access: Fiber-To-The-Home (FTTH).

 Core: State-of-art optical transmission and switching.


 The technological evolution leads to price reduction → Ubiquity.

 More and more devices are becoming computationally capable
and connected to the Internet (e.g. clothing, buildings).

 Inexpensive computing → Ubiquitous Computing (smart
environments and ambient intelligence).


Internet of Things
 Consequences of Ubiquitous Computing:
 Connectivity anywhere, anytime, in anyplace, to anyone.

 The rise of the NEDs (Network Enabled Devices) army.

 The appearance of the Internet of Things (IoT) and Real World
Internet (RWI).


Internet of Things
 Challenges and Requirements (1/3):
 Exponential growth in the number of sensors collecting real world
information → A flood of traffic on the network.

 Real world could be increasingly integrated to the virtual one,
making it possible to greatly increase the interaction between
them.

 Changes in real world objects could be reflected in virtual world –
Changes made to virtual objects can become real.

 User’s sensitive information will be collected, such as identity,
location and other contextualized information.


Internet of Things
 Flood of sensitive information → will push network scalability to
new limits.

 How to make this information safely available for innovative
applications?

 How to address billions of new nodes? Addressing and traceability
to sensors and actuators, e.g. in the case of a fire.

 Information needs to be contextualized to allow delivering to the
right destiny, at the right time (information freshness).


Internet of Things
 The need for energy-aware security → trust relations among
nodes.

 Semantic and context → smoke detector: fire or fireworks?

 NEDs mobility → ID/Loc splitting.

 Management and control → Autonomic technologies.

 RWI as a sensorial system for Future Internet.


Virtualization
 Exponential growth → diffuse substrate of digital technologies
composed by processing, storage, display and communication
resources.

 Much of the communication equipment today → become
computers, with CPUs, Operating Systems, etc.

 How to make this diffuse substrate of hardware resources
transparently and uniformly available to software?


Virtualization
 The roles of virtualization on FI:
 An elementary aspect of the architecture itself;

 To enable simultaneous architectures over the physical SN,
therefore creating a meta-architecture;

 To support experimentation with new architectures;

 To allow customizable service-aware networks, e.g. content-
networks;

 To allow “new business models for carriers and operators”,
(Nakao, 2009), e.g. virtual service operators.


Virtualization
 My definition of network virtualization:
 To create an abstraction (indirection) layer between network
equipment (routers, switches and radios) and network software,
such that communication resources can be used concurrently/
transparently/uniformly by different software instances.

 It allows multiple Virtual Networks (VNs) to share the same
Substrate Network (SN).

 A virtual network has several of virtual nodes connected by
physical and/or virtual links, thus forming a virtual topology.


Virtualization
 What we can do with such idea?

Isolated Transitive Overlaid
VN2
VN1 VN2 VN3 VN4 VN1 VN2 VN3 VN1
Substrate Network Substrate Network Substrate Network

Fonte: Hiroaki Harai, Akari Project.


Virtualization
 Scalability – How to support a large number of VNs?

 Manageability – How to manage a large number of VNs? How to
manage traffic?

 Multidomain/Multioperator – How to interoperate VNs? How to
span over multiple physical operators? Is it standardization
required?

 Selection/Admission/Routing – Virtual topology design and
physical entities assignment are NP-hard problems.


Virtualization
 Resources Exposure – How to describe resources?

 Security, Trust and Privacy – Possible attacks, security risks,
denial of service.

 Mobility – How to move virtual entities?

 Complexity – How to deal with the increasing complexity?
Autonomicity?


Virtualization
 Is it possible to apply virtualization on wireless networks?

 More difficult to be virtualized:
 E.g. interference, shadowing, multipaths, multiple access and
other aspects of the propagation environment.

 These difficulties do not mean that radio resources aren’t an
exception.

 Software Defined Radio (SDR) can be seen as a radio where
hardware resources are exposed and reconfigured by software.


Virtualization
 Some interesting things we can do with radio virtualization:

Isolated Virtual MAC
VMAC1 VMAC2
T1 T2 T3 PHY1 PHY2
Substrate Hardware Substrate Hardware

Transitive Generic MAC
MAC
T1 T2 T3 PHY1 PHY2
Substrate Hardware Substrate Hardware

2. Information, ID/Loc Splitting, Semantic, Context and Mobility
 Information-centric Approches
 ID/Loc Splitting
 Generalized Mobility
 Semantic, Context, Context-Awareness and Ontology
 Semantic Web


Information-Centric Approaches
 Information as a key ingredient in design.

 Information is in everywhere, i.e. contracts, location, police, IDs,
descriptors, naming, etc.


Information-Centrism
 Requirements and Challenges (1/5):
 To make information the center of design.
 “Information is everything and everything is information” (PSIRP, 2009).

 To represent persistently and consistently information by means of
Information Objects (IOs), which contains:
 Digital signatures, checksums, metadata, access rights, formats,
ontology, etc.

 To access information independently of its location.

 To name contents (or its representation).
 “Named content is a better abstraction for today’s communication
problems than named hosts.” (Van Jacobson, 2009).


 To adequately manage content → versioning, encodings, copies
of identical content.

 To use name resolution schemes to find out locators to named
content.

 To allow disruptive and consented communications, e.g. publish/
subscribe (pub/sub) paradigm.

 To enable anycast and multi path routing of previously located
information. To improve multicasting support.

 To efficiently distribute content, customizing and improving Quality
of Experience (QoE).


 To cache information to improve performance and efficiency.

 To enable efficient, semantic rich, context-based information
search and manipulation.

 To deal with information scope. “Policy is metadata. So is scope!”,
PSIRP.

 To deal with provenance, ontology and coherence as advocated
by Van Jacobson @ FISS 2009.

 To identify information uniquely.


 To rethink security from the information point of view → securing
information per se.

 To explore self-certifying names → cryptographic hash function
over the binary data.

 To provide secure rendezvous among information producers and
consumers.

 To verify publisher privacy before content publishing –
authenticate and authorize subscribers during rendezvous.


 To solve indirections (ID/Loc) dynamically, efficiently, generically
and robustly.

 To deal with scalability on information representation, searching,
naming resolution, location, routing, etc.

 To deal with multi level, multi domain environments.

 To autonomously manipulate content.


ID/Loc Splitting
 Future networks need to separate identifiers (ID) from locators
(Loc) → the so called ID/Loc splitting.

 This split is required not only for physical entities (e.g. hosts),
but also for virtual entities as well as for content.


ID/Loc Splitting
 To uniquely identify every entity in the network as well as
information → They can be moved, searched and localized
without change their identities.

 How to generate unique digital identifiers for real or virtual
entities?

 How to manage IDs in order to provide generalized mobility for
real or virtual entities?

 How to deal with privacy, anonymity and traceability?
 Unique IDs can provide information sources non-repudiation.


ID/Loc Splitting
 How to use accountability information to prevent or to punish
cyber crimes?
 Traceability based on persistent IDs discourage network misuse.

 How to manage the large number of IDs, their relationships and
lifecycles?
 There is a massive scalability problem here!

 How to manage credentials and their relations to IDs?
 Unique IDs enhances digital credentials.

 How to discovery IDs of real or virtual entities?


Generalized Mobility
 General mobility means to comprehensively support user,
terminal, service, application, virtual networks, information, and
other real and virtual entities mobility.


Semantic, Context, Context-Awareness and Ontology
 Semantic
 “Relating to meaning in language or logic”, Thesaurus Dictionary.

 Context
 (Giunchiglia, 1992) defines context as a “subset of the complete
state of an individual that is used for reasoning about a given
goal”.

 Situation
 (Baker et al., 2009): situation is a snapshot of related context
information at a certain time and space.


 Situation-Awareness
 According to (Baker et al., 2009) “... being aware of its physical
environment or situation and responding proactively and
intelligently based on such awareness”.

 Context-Awareness
 To be aware of relevant contexts.

 Ontology
 For (TripCom, 2008) “an ontology is a formal definition of
terminology and relationships among the terms in a computer-
processable form”.


 Requirements and Challenges:
 Situation and Context Awareness → RWI as a source for situation
and context information.

 Autonomicity → To enable a system/application/artifact to adapts
to environmental or goal changes → Ontologies for rules, goals,
regulations, etc.

 Context Distribution → Collaboration of context processing entities
using the publish/subscribe paradigm.


Semantic Web
 (Berners-Lee, 1999):
 Information meaning (semantic) + treatment = a web that can
“understand” what entities want.

 “We need to abstract from the syntax to semantics”.

 It is an autonomous knowledge web, including context-aware
applications and services composition.


3. Autonomic and Cognitive Technologies
 Introduction
 Autonomic Computing
 Cognitive Computing
 Autonomic Communications
 Cognitive Radio


Introduction
 (Wang, 2009) classifies computer technologies and systems as:
 Imperative → Based on Von Neumann architecture.

 Autonomic → “Goal-driven and self-decision-driven technologies
that do not rely on instructive and procedural information.”

 Cognitive → “Implements computational intelligence by
autonomous inferences and perceptions mimicking the
mechanisms of the brain.”


Autonomic Computing
 Why autonomic computing?
 Accelerated returns:
 Boom in diversity, scale and complexity.

 Human capability limitations:
 Highly stressful job and deep sense of failure.

 OPEX:
 Human resources are expensive.

 Rapid adaptation to the environment.


Autonomic Computing
 (IBM, 2001):
 A famous manifesto → autonomic computing.

 “Computing systems’ complexity appears to be approaching the
limits of human capability”.

 Bio-inspired → human autonomic nervous system governs various
functions without our awareness.

 Computational systems → manage themselves according to high-
level objectives outlined by human operators.

 Reduce human interference and OPEX.


Autonomic Computing
 (Kephart and Chess, 2001):

4 autonomic properties:
 Self-Configuration - To configure components and the system
itself to achieve high-level goals.

 Self-Optimization - To optimize proactively system resources and
other aspects in order to improve performance, efficiency, quality,
etc.

 Self-Healing - To detect, diagnose and repair localized problems
and failures.

 Self-Protection - To defend against attackers, threads or cascade
failures.


Autonomic Computing
 Autonomic Managers:
 Decentralized and cooperative.

 Interact each other and with human operators to obtain the
expected behavior for the system → self-emergent or “social”
behavior.

 Follows a bottom-up design approach, where basic functions
cooperate to achieve top level goals.

 Require gateways to managed resources.

 Use communication resources to exchange obtained knowledge.


Autonomic Computing
 Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K).

Autonomic Manager
Knowledge

Monitor Analyze Plan Execute

Managed Element

Adapted from (Kephart and Chess, 2001).


Autonomic Computing
 (Dobson et al., 2010):
 The most notable omission from IBM’s original vision is
autonomous elements communication.

 (Clark et al., 2003):
 To incorporate more autonomy in communication networks,
creating the so-called Knowledge Plane.

 (Smirnov, 2004)
 The idea of Situated and Autonomic Communication (SAC).


Autonomic Computing
 (Sterrit and Bustard, 2003):
 Self-awareness → aware of its internal states, skills, available/
unavailable resources
 Self-situation → aware of the situation of the external environment.
 Self-monitoring → automatically detect context changes in rules, goals
and other information.
 Self-adjustment → adapt appropriately to them.

 (Dobson et al., 2010):
 “Researchers have devised innovative autonomic solutions to individual
problems, but the larger, more difficult task of combining these point
solutions into autonomic systems remains.” → transcendence.


Autonomic Computing
 (EURESCOM, 2009):
 Operators environment → combine self-* properties in a gracefull and
holistic way.

 Interoperability → fundamental to deploy “open, end-to-end and
heterogeneous autonomic features”.


Cognitive Computing
 (Wang et al., 2006):
 Bioinpired → inference, perception and cognitive mechanisms of
the human brain as defined in the Layered Reference Model of
the Brain (LRMB).

 (Wang et al., 2010):
 Perspectives on AI (Artificial Intelligence), knowledge
representation, machine learning, role-based social computing.


Autonomic Communications
 (Clark et al., 2003):
 “to build a different sort of network that can assemble itself given
high-level instructions, reassemble itself as requirements change,
automatically discover when something goes wrong, and
automatically fix a detected problem or explain why it cannot do
so.”

 (Smirnov, 2004):
 “radical paradigm shift towards a self-organising, self-managing
and context-aware autonomous network – considered in a
technological, social and economic context – to respond to the
increasingly high complexity and demands now being placed on
the Internet”.


 (Dobson et al., 2006):
 “... rethinking of communication, networking, and distributed
computing paradigms to face the increasing complexities.”

 Context awareness and semantics → important to improve
network operation.


 Self-awareness, Self-situation, Self-monitoring, Self-adjustment.

 Self-awareness → introspective to the own node status and
capabilities, e.g. antenna, bandwidth, laser.

 Self-situation or environment-aware → sensing from RWI, e.g.
primary operators in cognitive radio.

 Adequate self-situation → adequate reasoning in the control loop.


 Information contextualization → relevance, self-situation/self-
awareness, sound reasoning.

 Cooperation → common objectives, self-management, self-
emergent behavior, quality and scalability of information gathering,
privacy, security.

 Stability → self-stable, i.e. to avoid instability.

 Detail level and timely sharing → Information needs to be
collected, filtered and distributed to cooperating nodes, in the right
time, with right context.


 Incomplete knowledge → part of the real world, c.f. cognitive
informatics.


Cognitive Radio
 Joseph Mitola III:
 Software Defined Radio (SDR) in 1991;
 and Cognitive Radio (CR) in 1999.

 Software Defined Radio (SDR):
 A radio where PHY signal processing is software-defined or even
software based. It is capable to reconfigure its parameters and
even functionalities.

 Cognitive Radio (CR):
 Reconfigurable SDR to assert operational decisions accordingly
to the state of the radio environment as well as the available
physical hardware capabilities.


Cognitive Radio
 Opportunistically share radio spectrum → primary and secondary
network operators/users.

 Dynamically manage access to the radio frequency spectrum.

 Sensing → constantly search for new bandwidth opportunities.

 Planning & Reasoning → When an opportunity is found, a
decision must be made.

 Primary-users-aware → If a primary signal is detected, secondary
transmission should stop to avoid interference.


Cognitive Radio
 Cooperation → it helps:
 To avoid shadowing areas and other phenomena that can cause false
opportunities detection as well as interference.
 To establish trustable networks in order to improve security.
 To avoid attackers.

 Autonomicity → reduces human interference, takes advantage of
software platform, deals with repetitive tasks.


4. Security, Privacy and Trust
 FI: Security, Privacy and Trust
 Research Projects


FI: Security, Privacy and Trust
 Built in or inherent;

 Current communication model (receive all) → consented
communications, e.g. publish/subscribe paradigm;

 People must trust not only in the network, but also in its entities;

 Establishment of trusted networks → entities, services, users,
hardware.


 How to evaluate trust and reputation?

 Reputation → monitoring and policing is necessary to determine
trustworthiness of entities;

 Dependability → trusted parties need to be ascertained;

 Risk announcements → intuitive, contextualized dissemination;


 Relationships → identities, trust relations, credentials, reputation;

 Privacy → To help users to protect and preserve their privacy;

 Anonymity and accountability → relation to governments and
cyber laws;

 Tussle networking (Clark) → contradictory requirements,
evolvability, tussle accommodation;


 To identify, assess, monitor, analyze and sort risks, vulnerabilities
and threats;

 Management of identities, credentials and reputation is required;

 Autonomicity → to manage complexity of security, privacy, trust,
dependability, decision, risk, etc.;

 Self-awareness, situation awareness and semantics of
contextualized information are also requirements;

 What about the role of standardization/regulation?


 Huge amount of data?

 (X-ETP, 2010):
 Multi-domains, multi-level;
 Protection of credentials and ID-management;

 To include legal issues on the network?

 Proactive → distributed massive attacks and unpredicted
vulnerabilities and threats;


5. Services and Applications
 Service-centric Approaches
 Internet of Services
 Benefits for Users
 Digital Business Ecosystems


Service-centric Approaches
 Software design → changing from component-based to service
oriented design: service-centrism.
 E.g. SOA (Service Oriented Architecture).

 The idea → applications are flexibly and dynamically
constructed by the composition of distributed software services
or utilities.

Dependability will be a Scalability and cross-domain
problem and will require
App
operation will be needed.
appropriate treatment. S8 S9
S7 S6 S5
S4 S3 S2 S1

Basic building blocks become available to compose other services.

 Life-cycling → dynamic, distributed and cross-domain;

 Seamless → service describing, publishing, discovering and
negotiating will be necessary;

 How to search, discover and select candidate services?

 Which atributes are representative? Context? Semantic?

 How to make attributes searchable? Publishing in divulgation
services?


 Negotiation → necessary to establish SLAs (Service Level
Agreements);

 Admission control → is there available resources to attach the
desired service to one more application?

 Admission installation → proceeds to configure the services;

 Service monitoring, logging and exception handling;

 Management → Autonomicity?


 Self-Adaptation → changes in application/business:
 Inclusion or elimination of participating services;
 SLAs adequacy;
 Context and semantics;
 Rules, objectives, goals;
 Business processes adequacy.

 Turn off → application resources release;


Internet of Services
 Above a certain level of abstraction everything can be viewed as
a service → Internet of Services.

 (Villasante, 2009):
 “Internet of Services – Supporting the service economy (70% of
GDP in modern societies)”.

 (Cross-ETP, 2009):
 “The term Internet of Services is an umbrella term to describe
several interacting phenomena that will shape the future of how
services are provided and operated on the Internet”.


Digital Business Ecosystems
 Dynamic service compose-ability → integrate business
processes with applications and services, creating the so called
Digital Business Ecosystems (DBEs).

 DBEs → the new savannah.


6. Simplicity, Sustainability and Evolvability
 Simplicity
 Sustainability
 Evolvability


Simplicity
 To call attention on how difficult it is to design with simplicity we
can evoke Leonardo Da Vinci’s:
 “Simplicity is the ultimate sophistication”.

 Or as Einstein said:
 "Make everything as simple as possible, but no simpler.“

 Simplification of integrated technologies is one of the concerns
in Japanese Akari project.


Sustainability
 Sustainability can be defined as the property of maintaining a
certain level/situation in the course of time.

 Akari also aims to project a sustainable network, capable to
evolve and support information society requirements in the next
decades.


Evolvability
 Evolvability is a definition related to biological systems.

 (Rowe & Leaney, 1997):
 “the ability of a system to adapt in response to changes in its
environment, requirements and implementation technologies.”

 Accommodating tussles → A tool for evolvability


7. Artificial Intelligence and Other Bio-inpired ICT
 AGI
 Bio-inspired ICT


AGI
 (Wang)(Kurzweil, 2005)(Dartmouth Meeting, 1956):
 Artificial Intelligence (AI) came up with the idea of building thinking
machines similar to humans.

 Due to various issues, this did not happen. The expectations were
too high and incorrectly timed.

 “AI winter” → many companies failed in 80 years because
profits did not materialized.

 “narrow AI” → scope changed to domain-specific problems.


AGI
 Little value → devalue the “narrow AI” success achieved on last
decades.

 (Wang):
 “narrow AI” segmentation → led to research fragmentation and
loss of identity.

 AI rarely gets the credit for their accomplishments.

 Since 2004 → interest for general-purpose AI research is back.

 “AGI research treats "intelligence" as a whole.


AGI
 AGI vs. FI?
 Cognitive aspects → need more than specific AI, possibly
requiring results from AGI.

 Examples: cognitive radio networks, digital ecosystems, virtual
entities, applications and services orquestration, semantics and
contextualization, etc.

 "narrow AI?" → continues to have a role in specific aspects.


Bio-inspired ICT
 Some inovative bio-inspired approaches for ICT:
 Artificial Life
 Digital Evolution
 Digital Ecosystems
 Evolvable Hardware
 Artificial Embryogeny
 Artificial Immune Systems
 Swarm Intelligence
 Social Insects
 Brain Inspired ICT
 Bio-Chemistry ICT
 Molecular Scale Networks


Thank You!
Antônio Marcos Alberti
antonioalberti.blogspot.com


References
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 Saracco R (2009) Telecommunications Evolution: The Fabric of
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 Akari (2008) New Generation Network Architecture AKARI
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 Cross-ETP (2009) The Cross-ETP Vision Document. European
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