Future Internet Tutorial - Requirements and Challenges - IWT 2011

© Antônio M. Alberti 2011

Future Internet: New Network Architectures and Technologies

Part III - Requirements & ChallengesAntônio Marcos Alberti


Outline1. Substrate Resources and Its Integration with Software2. Information, ID/Loc Splitting, Semantic, Context and Mobility3. Autonomic and Cognitive Technologies4. Security, Privacy and Trust5. Services and Applications6. Simplicity, Sustainability and Evolvability7. 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

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

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


Capacity and Ubiquity (Kurzweil, 2005):

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.


Capacity and Ubiquity 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.


Capacity and Ubiquity 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.


Capacity and Ubiquity 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.



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).



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?

Fonte: Hiroaki Harai, Akari Project.

Substrate Network

VN1 VN2 VN3 VN4

Isolated

Substrate Network

VN1 VN2 VN3

Transitive

Substrate Network

VN1VN2

Overlaid


Virtualization Challenges and Requirements (1/2):

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 Challenges and Requirements (2/2):

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:

Substrate Hardware

T1 T2 T3

Isolated

Substrate Hardware

T1 T2 T3

Transitive

Substrate Hardware

MAC

Generic MAC

PHY1 PHY2

Substrate Hardware

VMAC1

Virtual MAC

PHY1 PHY2VMAC2


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 Requirements and Challenges (1/2):

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 Requirements and Challenges (2/2):

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.


Semantic, Context, Context-Awareness and Ontology 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”.


Semantic, Context, Context-Awareness and Ontology 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

Managed Element

Adapted from (Kephart and Chess, 2001).

Knowledge

Monitor Analyze Plan Execute


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 Requirements and Challenges (1/2):

(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 Requirements and Challenges (2/2):

(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”.


Autonomic Communications (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.


Autonomic Communications Requirements and Challenges (1/3):

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 Requirements and Challenges (1/2):

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 Requirements and Challenges (2/2):

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 Requirements and Challenges (1/5):

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.

App

S8 S9

S7 S6 S5

S4 S3 S2 S1

Dependability will be a problem and will require appropriate treatment.

Scalability and cross-domain operation will be needed.

Basic building blocks become available to compose other services.


Service-centric Approaches Requirements and Challenges (1/3):

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.

(Kurzweil, 2005): 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 Albertiantonioalberti.blogspot.com


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Future Internet Tutorial - Requirements and Challenges - IWT 2011

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