Lecture 1: Introduction to Self- Organization

Transcription

1 Lecture 1: Introduction to Self- Organization Self-Organizing Page 1

2 Introduction to Self-Organization Why is it important? => Motivation for Self-organization What does it mean? => Definition of Self-organization How does it work in practice? => Examples for Self-organization in Communication Systems Page 2

3 Why a course on self-organizing systems? Page 3

4 Traditional Client-Server Motivation- Internet Current and future Internet of things Virtualization (i.e. Peer-to-Peer (P2P) and overlay) Page 4

5 Motivation- Internet Example: data storage/retrieve problem! Where to store and how to find certain data element (file)? Page 5

6 Client/Server (Server Stores the Data) Well known approach Server is a data source I need data titanic.avi Clients request data from server Sever is a powerful computer Clients have usually lower capabilities Page 6

7 Unstructured P2P (Server knows info.) A way to share files with others Users upload their list of files to server You send queries to for files of interest (e.g. Song) server replies with IP address of users with matching files You connect directly to user A to download file Example Napster, Skype I need data titanic.avi I have data titanic.avi Page 7

8 Structured P2P (DHT) Decentralized Autonomous components Mapping E.g. Map Filename to Node ID Nodes have partial view about the network Distributed Hash Table -DHT I need data titanic.avi Hash( titanic.avi )=7 Get(7) I have data titanic.avi Hash( titanic.avi )=7 Put(7,titanic.avi) Page 8

9 Motivation- Internet Think about! (Which system can be self-organizing?) Which system has a central entity? Where do we have single node of failure? What about the capabilities of the computers (server)? Page 9

10 Comparison Client Server Unstructured P2P Structured P2P Scalability Bad Middle Good Cost Bad Middle Good Reliability Good Bad Bad Single node of failure Bad Middle Good Page 10

11 Motivation- Next Generation Mobile Communication Systems Mobile communication systems in the past Manual configuration Manual adaptation (optimization) Manual repair Central controller (e.g. RNC) Mobile communication systems in future Large, complex and dynamic No central controller (several functions are moved to BS) Page 11

12 Motivation- Next Generation Mobile Communication Systems femtocell networks Massive deployment of user-operated home-base stations (femto-access points) for cellular communications demands for decentralized radio resource allocation Lacking any central authority having complete control of the network topology, the system must incorporate selforganization, self-healing capabilities Page 12

13 Transition to LTE/SAE: Architecture Base station Base station PSTN Base station GSM RAN Base station controller MSC GSM Core (Circuit switched) GMSC HLR AuC EIR e- e- node B node B e- node B S-GW SGSN Radio network controller E- UTRAN EPC GPRS Core (Packet Switched) P-GW IMS GGSN Internet Page 13

14 Motivation- Ad hoc Networks Example: Wireless Sensor Networks Massive deployment of sensors in difficult to reach areas (e.g. deployment by aircraft) Self-organization capabilities in terms of sensing and internode communications All nodes initially have the same configuration (No IP or MAC address) Robustness to node failure and self-healing mechanisms are important Limited resources Energy efficient Page 14

15 Motivation for Self-Organization Problem of today s networks Heterogeneity Dynamics Scalability Method: Self-organization Fast, autonoms reaction to problems Automation of control Distributed control Some application scenarios: Severe network impact due to disasters Energy savings Privately operated femto cells Page 15 15

16 Our Application Interest in Self-Organization R Reconfigurable Radio Interfaces S Self-organized Service Recovery I Decentralized Information Management T Page 16 Cognitive Management of Transport Resources

17 Research Topics Secure Network Operation How can access to adhoc-deployed communication infrastructure be restricted? Innovative authentication techniques based on localization? How to ensure secure localization? How to configure/manage access rights, cryptographic keys etc.? Page 17 17

18 Research Topics Integration of UAVs Self-organized integration of mobile platforms (land-based and airborne) image source: Page 18 18

19 Self-org is a vital need for complex systems The term complex system formally refers to a system of many parts which are coupled in a nonlinear fashion. A linear system is subject to the principle of superposition, and hence is literally the sum of its parts, while a nonlinear system is not. When there are many nonlinearities in a system (many components), its behavior can be as unpredictable as it is interesting. Local rules, which describe the behavior of each entity in the system, lead to complex global states. Page 19

20 Are Self-Organizing Techniques Unique Solutions? Page 20

21 NOT Are Self-Organizing Necessary Techniques Unique Solutions? Remember Client-Server and P2P! What are they good for? Page 21

22 Drivers for Self Organizing Networks Technological drivers The complexity of systems Market drivers Reduce Capital EXpenditures (CAPEX) For example applying self-configuration algorithms reduces human effort in the installation Reduce Operational EXpenditure (OPEX) For example applying self-optimization algorithms reduces the power consumption Page 22

23 Non-living systems Sand dune ripples Natural Systems Page 23

24 Natural Systems Living systems Zebra stripes Page 24

25 Living systems (social) Ants Natural Systems Page 25

26 Living systems (social) School of fish Natural Systems Page 26

27 Self-Organization and Emergence Emergent phenomena pattern / function / property /structure Examples: Sand dune ripples Zebra stripes Emergence of consciousness in human Emerges from neurons interactions Shortest path to food found by foraging ants Engineered systems: TSP: shortest path to visit all cities Page 27

28 Self-Organization and Emergence A self-organizing system produces complex organization from randomness based on local interaction and without external intervention Page 28

29 Self-Organization and Emergence Emergent Phenomena do not appear on the individual component level but on the global level Emergent Phenomena is far beyond the capabilities of individual components Page 29

30 Self-Organization-definition Self-organization in Engineered systems Selforganization is the process enabling a system to change its organization in case of environmental changes without explicit external command. Strong self-organizing systems are those systems where there is reorganization with no explicit central control, either internal or external. Weak self-organizing systems are those systems where, from an internal point of view, there is re organization under an internal central control or planning. Page 30

31 Properties of self-organizing systems No central entity No external controller Global behavior results from local interaction No global pattern / recipe / rule to refer to Page 31

32 Capabilities of Self-Organizing systems Self-configuring The process where newly deployed components are configured by automatic installation procedures to get the necessary basic configuration for system operation Self-optimization Ability of the system to optimize the local operation parameters according to global objectives Self-healing Methods for changing configurations and operational parameters of the overall system to compensate failures Self-protection System automatically defends against malicious attacks or cascading failures. It uses early warning to anticipate and prevent system wide failures [Kephart 03] Page 32

33 Limitations of self-organizing approaches Controllability Cross-mechanism interference Difficult assessment of the mechanisms Large scale Dynamic Complex Unpredictable environment New software engineering approaches are needed Page 33

34 Natural Self-Organization and Bio-Inspired Echolocation mechanisms in many animals have always been a source of inspiration for radar waveform design The chirp acoustic signals emitted by bats/dolphins are very similar to the radar waveform Page 34

35 Natural Self-Organization and Bio-Inspired Principles of echolocation (bio-sonar) The 18th century Italian scientist Lazzaro Spallanzani had, by means of a series of elaborate experiments, concluded that bats navigate by hearing and not by vision Page 35

36 Bio-Inspired Routing based on Ants behavior Page 36

37 Design Steps of Bio-inspired Solutions Analogy Study the analogy between the biological system and our system (ICT problem) Modeling and simulation Obtain a realistic model Testing Explore the performance Implementation Implement the system Page 37

38 Engineering of Self-Organizing systems Positive and Negative Feedback Direct and indirect interaction Decentralized Control Page 38

39 Positive Feedback Initial change in a system is reinforced in the same direction as initial change Towards amplification Implies changes in the system Ex: Fish nesting Initial change = some fishes nest close to each other Positive feedback rule = I nest where other similar individuals nest Increased aggregation of fishes at the same place Page 39

40 Negative Feedback Perturbation applied to system triggers response that counteracts the perturbation Towards stabilisation Avoids fluctuations Ex: maintain a constant distance to your neighbors Negative feedback = A bird is close to flock (swarm) Response = Decrease speed Result = Avoid collisions with neighbors Page 40

41 Positive Feedback coupled with Negative Feedback Positive feedback pushes system towards its limits Negative feedback or physical constrains provide inhibition and maintain system under control Ex: Fish nesting Rule = I nest where other similar individuals nest unless there are too much fishes Page 41

42 Direct Communication Schools of fishes Information acquired from neighbour Page 42

43 Indirect Communication Information acquired from shared local environment and work-in-progress : Stigmergy Social Insects Ants deposit pheromone along traveled path which is used by other ants to follow the trail. This kind of indirect communication via the local environment is called stigmergy Page 43

44 Decentralized Control Coordination of work without central decisions Self-organization mechanisms are (mostly) based on decentralized architectures of information flow No instruction issued from leaders Individuals gather information (directly or not) and decides what to do Page 44

45 Decentralized Control Two kinds of engineered systems with decentralized control Case 1: Large set of autonomous components, pertaining to the same system and providing as a whole expected properties, or functions. Engineering with emergent functionality in mind Case 2: Large set of autonomous components, spontaneously interacting with each other, for possibly independent or competing reasons. No expected emergent global function or properties In both cases, autonomous components may be Heterogeneous dynamically joining and leaving the system. Page 45

46 Dynamic Change Continual interactions among components Components join and leave system at any time Dynamic systems Ants foraging Skype system of users Page 46

47 Research Directions Self-Organizing methods to solve problems in next generation networks Evaluation of self-organizing methods Not discovered bio-inspired approaches Page 47

48 Summary (what do I need to know) Why self-organizing systems? What is emergence phenomena? What are the characteristics of self-org systems? What are the advantages and disadvantages? What do we mean by bio-inspired approaches? What is Positive and Negative Feedback? What is direct and indirect communication? Page 48

49 References Slides Evaluation of self-organizing systems using quantitative measures by Hermann de Meer, Richard Holzer, Patrick Wüchner Slides Adaptive Systems by Giovanna Di Marzo Serugendo Slides Bio-Inspired Signal Processing by Sergio Barbarossa Slides Self-Organization in Sensor and Actor Networks by Falko Dressler F. Dressler, Self-Organization in Sensor and Actor Networks. John Wiley & Sons, December 2007 E. Bonabeau, M. Dorigo, and G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems Santa Fe Institute Studies on the Sciences of Complexity. Oxford University Press, UK, Page 49

50 References E. Bonabeau, M. Dorigo, and G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems Santa Fe Institute Studies on the Sciences of Complexity. Oxford University Press, UK, S. Camazine, J.-L. Deneubourg, Nigel R. F., J. Sneyd, G. Téraulaz, and E.Bonabeau. Self-Organisation in Biological Systems. Princeton Studies in Complexity. Princeton University Press, J.H. Holland, Emergence from Chaos to Order. Oxford University Press, 1998 M. Wooldridge: An Introduction to Multi-Agent Systems. Wiley, 2003 L. M. de Castro: Fundamentals of Natural Computing Basic Concepts, Algorithms, and Applications. Chapman & Hall/CRC Page 50