Future Internet Tutorial - Requirements and Challenges - IWT 2011


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This is the third part of a Future Internet Tutorial presented at IWT 2011. See: www.inatel.br/iwt

The Internet has invaded most aspects of life and society, changing our lifestyle, work, communication and social interaction and giving us expectations about new forms of interactions and access to global knowledge. Application and user demands on the Internet are increasing with mobile technologies and media content. Nevertheless, the Internet today is a complex agglomerate of protocols that inherits the grown legacies of decades of patchwork solutions.

There is a common consensus that the Internet needs improvement. Nevertheless, there is not yet a shared vision on how this may happen. As a direct consequence research programs have started worldwide to re-think traditional Internet design principles and to come up with new architectural concepts for the so-called Future Internet (FI).

The Future Internet Tutorial provides an overview of Future Internet research directions and trends. It presents the Future Internet research initiatives around the world and the efforts to establish experimental facilities for FI research. The tutorial gives an introduction to new Future Internet architectures that are currently under discussion and related technologies. Among the approaches discussed are addressing and routing concepts, adaptability, autonomicity, self-*, *-aware and manageability, virtualization, neutrality, openness, diversity, extendibility, flexibility and evolvability. The tutorial also presents some interdisciplinary aspects related to artificial general intelligence and bio-inspired ICT.


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Future Internet Tutorial - Requirements and Challenges - IWT 2011

  1. 1. Future Internet: New Network Architectures and TechnologiesPart III - Requirements & Challenges Antônio Marcos Alberti © Antônio M. Alberti 2011
  2. 2. 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 © Antônio M. Alberti 2011
  3. 3. 1. Substrate Resources and Its Integration with Software Technology Evolution Capacity and Ubiquity Internet of Things Real World Internet Virtualization © Antônio M. Alberti 2011
  4. 4. Technology Evolution Moores 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 © Antônio M. Alberti 2011
  5. 5. 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 © Antônio M. Alberti 2011
  6. 6. 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. © Antônio M. Alberti 2011
  7. 7. 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. © Antônio M. Alberti 2011
  8. 8. 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. © Antônio M. Alberti 2011
  9. 9. 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). © Antônio M. Alberti 2011
  10. 10. 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). © Antônio M. Alberti 2011
  11. 11. 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. © Antônio M. Alberti 2011
  12. 12. Internet of Things Challenges and Requirements (2/3):  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). © Antônio M. Alberti 2011
  13. 13. Internet of Things Challenges and Requirements (3/3):  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. © Antônio M. Alberti 2011
  14. 14. 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? © Antônio M. Alberti 2011
  15. 15. 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. © Antônio M. Alberti 2011
  16. 16. 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. © Antônio M. Alberti 2011
  17. 17. 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. © Antônio M. Alberti 2011
  18. 18. 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. © Antônio M. Alberti 2011
  19. 19. 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? © Antônio M. Alberti 2011
  20. 20. 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. © Antônio M. Alberti 2011
  21. 21. Virtualization Some interesting things we can do with radio virtualization: Isolated Virtual MAC VMAC1 VMAC2 T1 T2 T3 PHY1 PHY2 Substrate Hardware Substrate Hardware © Antônio M. Alberti 2011 Transitive Generic MAC MAC T1 T2 T3 PHY1 PHY2 Substrate Hardware Substrate Hardware
  22. 22. 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 © Antônio M. Alberti 2010
  23. 23. Information-Centric Approaches Information as a key ingredient in design. Information is in everywhere, i.e. contracts, location, police, IDs, descriptors, naming, etc. © Antônio M. Alberti 2010
  24. 24. 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). © Antônio M. Alberti 2010
  25. 25. Information-Centrism Requirements and Challenges (2/5):  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). © Antônio M. Alberti 2010
  26. 26. Information-Centrism Requirements and Challenges (3/5):  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. © Antônio M. Alberti 2010
  27. 27. Information-Centrism Requirements and Challenges (4/5):  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. © Antônio M. Alberti 2010
  28. 28. Information-Centrism Requirements and Challenges (5/5):  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. © Antônio M. Alberti 2010
  29. 29. 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. © Antônio M. Alberti 2010
  30. 30. 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. © Antônio M. Alberti 2010
  31. 31. 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? © Antônio M. Alberti 2010
  32. 32. Generalized Mobility General mobility means to comprehensively support user, terminal, service, application, virtual networks, information, and other real and virtual entities mobility. © Antônio M. Alberti 2010
  33. 33. 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. © Antônio M. Alberti 2010
  34. 34. 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”. © Antônio M. Alberti 2010
  35. 35. 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. © Antônio M. Alberti 2010
  36. 36. 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. © Antônio M. Alberti 2010
  37. 37. 3. Autonomic and Cognitive Technologies Introduction Autonomic Computing Cognitive Computing Autonomic Communications Cognitive Radio © Antônio M. Alberti 2010
  38. 38. 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.” © Antônio M. Alberti 2010
  39. 39. 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. © Antônio M. Alberti 2010
  40. 40. 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. © Antônio M. Alberti 2010
  41. 41. 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. © Antônio M. Alberti 2010
  42. 42. 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. © Antônio M. Alberti 2010
  43. 43. Autonomic Computing Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K). Autonomic Manager Knowledge Monitor Analyze Plan Execute Managed Element Adapted from (Kephart and Chess, 2001). © Antônio M. Alberti 2010
  44. 44. 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). © Antônio M. Alberti 2010
  45. 45. 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. © Antônio M. Alberti 2010
  46. 46. 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”. © Antônio M. Alberti 2010
  47. 47. 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. © Antônio M. Alberti 2010
  48. 48. 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”. © Antônio M. Alberti 2010
  49. 49. 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. © Antônio M. Alberti 2010
  50. 50. 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. © Antônio M. Alberti 2010
  51. 51. Autonomic Communications Requirements and Challenges (2/3):  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. © Antônio M. Alberti 2010
  52. 52. Autonomic Communications Requirements and Challenges (3/3):  Incomplete knowledge → part of the real world, c.f. cognitive informatics. © Antônio M. Alberti 2010
  53. 53. 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. © Antônio M. Alberti 2010
  54. 54. 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. © Antônio M. Alberti 2010
  55. 55. 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. © Antônio M. Alberti 2010
  56. 56. 4. Security, Privacy and Trust FI: Security, Privacy and Trust Research Projects © Antônio M. Alberti 2010
  57. 57. 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. © Antônio M. Alberti 2010
  58. 58. FI: Security, Privacy and Trust Requirements and Challenges (2/5):  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; © Antônio M. Alberti 2010
  59. 59. FI: Security, Privacy and Trust Requirements and Challenges (3/5):  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; © Antônio M. Alberti 2010
  60. 60. FI: Security, Privacy and Trust Requirements and Challenges (4/5):  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? © Antônio M. Alberti 2010
  61. 61. FI: Security, Privacy and Trust Requirements and Challenges (5/5):  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; © Antônio M. Alberti 2010
  62. 62. 5. Services and Applications Service-centric Approaches Internet of Services Benefits for Users Digital Business Ecosystems © Antônio M. Alberti 2010
  63. 63. 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. © Antônio M. Alberti 2010
  64. 64. 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? © Antônio M. Alberti 2010
  65. 65. Service-centric Approaches Requirements and Challenges (2/3):  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? © Antônio M. Alberti 2010
  66. 66. Service-centric Approaches Requirements and Challenges (3/3):  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; © Antônio M. Alberti 2010
  67. 67. 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”. © Antônio M. Alberti 2010
  68. 68. 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. © Antônio M. Alberti 2010
  69. 69. 6. Simplicity, Sustainability and Evolvability Simplicity Sustainability Evolvability © Antônio M. Alberti 2010
  70. 70. 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. © Antônio M. Alberti 2010
  71. 71. 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. © Antônio M. Alberti 2010
  72. 72. 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 © Antônio M. Alberti 2010
  73. 73. 7. Artificial Intelligence and Other Bio-inpired ICT AGI Bio-inspired ICT © Antônio M. Alberti 2010
  74. 74. 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. © Antônio M. Alberti 2010
  75. 75. 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. © Antônio M. Alberti 2010
  76. 76. 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. © Antônio M. Alberti 2010
  77. 77. 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 © Antônio M. Alberti 2010
  78. 78. Thank You!Antônio Marcos Alberti antonioalberti.blogspot.com © Antônio M. Alberti 2010
  79. 79. References Kurzweil R (2005) The Singularity is Near: When Humans Transcend Biology, Viking Press, ISBN 0670033847. Saracco R (2009) Telecommunications Evolution: The Fabric of Ecosystems. Revista Telecomunicações INATEL 12(2):36-45. Akari (2008) New Generation Network Architecture AKARI Conceptual Design. Project Description v1.1. Cross-ETP (2009) The Cross-ETP Vision Document. European Technology Platforms (ETPs) Cross Vision Document v1.0. © Antônio M. Alberti 2011
  80. 80. References Presser M, Daras P, Baker M, Karnouskos S, Gluhak A, Krco S, Diaz C, Verbauwhede I, Naqvi S, Alvarez F, Fernandez-Cuesta A (2008) Real World Internet Position Paper. Peterson L, Anderson T, Culler D, Roscoe T (2003) A Blueprint for Introducing Disruptive Technology into the Internet. SIGCOMM Computer Comm. Review 33(1):59-64. Peterson L, Shenker S, Turner J (2005) Overcoming the Internet Impasse through Virtualization. IEEE Computer 38(4): 34-41. GENI (2006) Technical Document on Wireless Virtualization. Global Environment for Network Innovations (GENI) Technical Report GDD-06-17. © Antônio M. Alberti 2011
  81. 81. References Jacobson V, Content-Centric Networking, Future Internet Assembly (FIA), Valencia, Spain, 2010. Rothenberg CE, Verdi FL, Magalhaes, M (2008) Towards a New Generation of Information-Oriented Internetworking Architectures. Re-Architecting the Internet, Madrid, Spain. Berners-Lee T, Hendler J, Lassila O (1999) The Semantic Web. Scientific American Magazine 23(1). Alberti A, (2010) Future Network Architectures: Technological Challenges and Trends, New Network Architectures: The Path to the Future Internet. Book Chapter. Springer-Verlag GmbH. DOI: 10.1007/978-3-642-13247-6_5. 2010. © Antônio M. Alberti 2010
  82. 82. References Jacobson V, Smetters D, Thornton J, Plass M, Briggs N, Braynard R (2009) Networking Named Content. CoNEXT’09, Rome, Italy. Ahlgren B, D’Ambrosio M, Dannewitz C, Marchisio M, Marsh I, Ohlman B, Pentikousis K, Rembarz R, Strandberg O, Vercellone V (2008) Design Considerations for a Network of In-formation. Re-Architecting the Internet, Madrid, Spain. Tarkoma S, Ain M, Visala K (2009) The Publish/Subscribe Internet Routing Paradigm (PSIRP): Designing the Future Internet Architecture. Towards the Future Internet, IOS Press. 4WARD (2010) Architecture and Design for the Future Internet: Second NetInf Architecture Description. Deliverable D6.2. © Antônio M. Alberti 2010
  83. 83. References Ohlman B, Ahlgren B, et al. (2010) Networking of Information: An Information-centric Approach to the Network of Future. ETSI Future Network Technologies Workshop. Niebert N (2008) Vision on Future Content Networks: A Networks and Media Joint Venture. Future Internet Assembly (FIA), Madrid, Spain. Paulson LD (2003) News Briefs - W3C Works on Semantic Web Proposal. Computer Magazine 36(11):20 Fensel D (2007) ServiceWeb 3.0. IEEE/WIC/ACM International Conf. on Intelligent Agent Technology, Fremont, USA. © Antônio M. Alberti 2010
  84. 84. References Baker N, Zafar M, Moltchanov B, Knappmeyer M (2009) Context-Aware Systems and Implications for Future Internet, Towards the Future Internet, IOS Press. Bicocchi N, Baumgarten M, Brgulja N, Kusber R, Mamei M, Mulvenna M, Zambonelli F (2010) Self-Organized Data Ecologies for Pervasive Situation-Aware Services: The Knowledge Networks Approach, IEEE Transactions on Systems, Man and Cybernetics – Part A: System and Humans, Vol. 40, No. 4. Dey A, Abowd D (2000) Towards a better understanding of context and context-awareness, Proc. ACM Conf. Human Factors Comput. Syst.—What, Who, Where, When and How of Context-Awareness, Hague, The Netherlands. © Antônio M. Alberti 2010
  85. 85. References Zimmermann A et al. (2005) Personalization and Context Management, User Modeling and User-Adapted Interaction 15, 3-4, pp. 275-302. Giunchiglia F (1992) Contextual Reasoning, Trento, Italy. Wang P (2004) Experience-Grounded Semantics: A theory for intelligent systems, Preprint submitted to Elsevier Science. Gruber T (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5:199–220. TripCom (2008) Ontology of EDIFACT Syntax and Semantics, Deliverable D7.2. © Antônio M. Alberti 2010
  86. 86. References Ben Yahia I, Bertin E, Crespi N (2007) Ontology-based Management Systems for the Next Generation Services: State- of-the-Art, presented in Networking and Services, 2007. ICNS Third International Conference and published in IEEE Transaction. Clark D, Partridge C, Ramming C, Wroclawski J (2003) A knowledge plane for the Internet, Proc. ACM SIGCOMM Conf., Karlsruhe, Germany, pp. 3–10. Siekkinen M, et al. (2007) Beyond the Future Internet – Requirements of Autonomic Networking Architectures to Address Long Term Future Networking Challenges, 11th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS07). © Antônio M. Alberti 2010
  87. 87. References Strassner J, (2008) The Role of Autonomic Networking in Cognitive Networks, Cognitive Networks: Towards Self-Aware Networks. John Wiley and Sons, Book Chapter 23-52. © Antônio M. Alberti 2010
  88. 88. References Jelger C (2009) Information Dispatch Points, NetArch Symposium Presentation, Ascona, Switzerland. Pollock J, Hodgson R (2004) Adaptive information: improving business through semantic interoperability, grid computing, and enterprise integration, John Wiley and Sons. © Antônio M. Alberti 2010
  89. 89. References Kephart JO, Chess DM (2003) The Vision of Autonomic Computing. IEEE Computer Magazine 36(1):41-50. Dobson S, Sterritt R, Nixon P, Hinchey M (2010) Fulfilling the Vision of Autonomic Computing. Computer Magazine 43(1): 35-41; Clark D, Partridge C, Ramming J, Wroclawski J (2003) A Knowledge Plane for the Internet. Proc. of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Comm., Karlsruhe, Germany; Smirnov M (2004) Autonomic Communication: Research Agenda for a New Communications Paradigm. Fraunhofer FOKUS technical Report; © Antônio M. Alberti 2010
  90. 90. References Dobson S, Denazis S, Fernández A, Gaïti D, Gelenbe E, Massacci F, Nixon P, Saffre F, Schmidt N, Zambonelli F, (2006) A Survey of Autonomic Communications. ACM Transactions on Autonomous and Adaptive Systems 1(2):223-259. Sterritt R, Bustard D W, (2003), Autonomic Computing—A Means of Achieving Dependability?, Proc. 10th IEEE Int’l Conf. and Workshop on the Eng. of Computer-Based Systems (ECBS 2003), IEEE Press:247-251. Chaparadza R, (2010), Can Autonomicity help Migration, and what could be a possible Evolution Path?, FIA‐GHENT: Migration Session, December 2010. © Antônio M. Alberti 2010
  91. 91. References Cross-ETP (2009) The Cross-ETP Vision Document. European Technology Platforms (ETPs), Cross Vision Document v1.0. Clarke J (2008) Trust & Identity in the Future Internet, Presentation at FIA. X-ETP (2010) Future Internet Strategic Research Agenda, Version 1.1. PICOS (2008) Taxonomy, Deliverable D2.1 Version 1.0. RFC 4949 (2000), Internet Security Glossary, IETF Request for Comments 2828. © Antônio M. Alberti 2010
  92. 92. References Chaum D (1985) Security without Identification: Transaction Systems to make Big Brother Obsolete, Communications of the ACM 28/10 1030-1044. Avizienis A, Laprie J, Randell B, Landwehr C, Basic concepts and taxonomy of dependable and secure computing, IEEE Transactions on Dependable and Secure Computing 1 (1). Pfitzmann A, Hansen M (2010), A terminology for talking about privacy by data minimization: Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management, Available online at http://dud.inf.tu-dresden.de/ Anon_Terminology.shtml/. © Antônio M. Alberti 2010
  93. 93. References Fischer-Hübner S, Hedbom H (2008) D14.1.c – PRIME Framework V3. Public Project Deliverable. Luhmann, N (1979) Trust and Power, Chichester, Wiley. RISEPTIS (2009) Trust in the Information Society, Report of the Research and Innovation for SEcurity, Privacy and Trustworthiness in the Information Society. Campolargo, M (2010) Trust in the Information Society, Presentation at “Trust in the Information Society” Conference. © Antônio M. Alberti 2010
  94. 94. References European Commission, “The Future of the Internet: A Compendium of European Projects on ICT Research Supported by the EU 7th Framework Programme for RTD”, 2008. Pedrinaci, C., “Lightweight Semantic Annotations for Services on the Web”, SSAIE 2009. Ristol, S., “Enabling a Web of Billions of Services”, SW 2009. S-Cube, “The S-Cube Book”, August 2010. De Panfilis, S., "FISO architecture of the Future Internet", FIA Workshop– FISO Session May 2009. © Antônio M. Alberti 2010
  95. 95. References Gittler, F, "NEXOF: An Approach for Service‐based System Architectures", 2nd International SOA Symposium, October 2009. Pasic, A., “Delivering Building Blocks for Internet of Services: Trust, Security, Privacy and Dependability”, Book Chapter, New Network Architectures, 2010. Benko, B. K., “Autonomic Communication Elements and the ACE Toolkit”, ACE Toolkit tutorial, Milan, November 2008. Baresi, L., Di Ferdinando, A., Manzalini, A., Zambonelli, F., “The CASCADAS Framework for Autonomic Communications”, Autonomic Communication, Springer, Heidelberg, 2009. © Antônio M. Alberti 2010
  96. 96. References Rowe D, Leaney JR (1997) Evaluating evolvability of computer based systems architectures - an ontological approach”, Proc. International Conference on the Engineering of Computer Based Systems, 1997. © Antônio M. Alberti 2010
  97. 97. References Wang P Artificial General Intelligence: A Gentle Introduction, Available online at: http://sites.google.com/site/narswang/home/agi- introduction/ Kurzweil R (2005) The Singularity is Near: When Humans Transcend Biology, Viking Press, ISBN 0670033847. Dartmouth Meeting (1956) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, Dartmouth College in Hanover, New Hampshire, Available online at: http:// www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html/ Langton C What is Artificial Life?, Available online at http:// www.biota.org/papers/cglalife.html/ © Antônio M. Alberti 2010
  98. 98. References McKinley P, Cheng B, Ofria C, Knoester D, Beckmann B, Goldsby H (2008) Harnessing Digital Evolution, IEEE Computer Magazine. Nachira F (2006) How ICT research supports Innovation Ecosystems and SMEs, UEAPME workshop. Briscoe G, De Wilde P (2006) Digital Ecosystems: Evolving Service-Orientated Architectures, Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems. © Antônio M. Alberti 2010
  99. 99. References Tempesti G, Mange D, Mudry P, Rossier J, Stauffer A (2007) Self-Replicating Hardware for Reliability: The Embryonics Project, ACM Journal on Emerging Technologies in Computing Systems (JETC), Vol. 3 Issue 2. Guedj D (2010) Future and Emerging Technologies Proactive Initiatives in FP7 call 6, Information Day, ICT Call 6 Brussels. © Antônio M. Alberti 2010