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DIVERSIFY

DIVERSIFY. Ecology-inspired software diversity for distributed adaptation in CAS. 1 slide about the main idea / challenge 1 slide about objectives 2 slides about budget 1 slide about IP 3 slides about impact ( meta design, adaptive systems , soft diversity )

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DIVERSIFY

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  1. DIVERSIFY Ecology-inspired software diversity for distributed adaptation in CAS

  2. 1 slide about the main idea / challenge • 1 slide about objectives • 2 slides about budget • 1 slide about IP • 3 slides about impact (meta design, adaptive systems, soft diversity) • 1 slide about WP2 • 1 slide about advances in soft diversity (update slide 24)

  3. Collaborative adaptive systems • Large-scale • Open • Dynamic • Eternal • Heterogeneousenvironments • Face unpredictable situations

  4. CASs are a form of complex system

  5. An essential property: diversity

  6. Main idea • Diversity is an essential characteristic of complex systems to adapt to unpredicted changes in their environment • Ecosystems, economical systems, social systems, etc. • CASs are deployed in environments that evolve in uncontrolled and unpredicted ways BUT • Software diversity remains very little explored as an insurance principle to adapt to changes

  7. DIVERSIFY brings together researchers from the domains of software-intensive distributed systems and ecology in order to translate ecological concepts and processes into software design principles

  8. Consortium

  9. Ecologicalboard B. Kunin (Univ. of Leeds) M. Hutchings (Univ. of Sussex) C. Melian (EAWAG) E. Thébault (CNRS)

  10. Objective DIVERSIFY aims at formalizing and experimenting new models and synthesis mechanisms for software diversity in collaborative adaptive systems, based on the ecological concept of biodiversity. The goal is to increase adaptive capacities in the face of structural and environmental variations.

  11. WP structure

  12. Progress in software engineering • Software diversity • synthesis and spontaneousemergence of software diversity • Dynamic adaptation • leveragingdiversity to reachspecific goals • Distributed adaptation • models@runtime for the collaboration of heterogeneous, distributed software entities

  13. Expected impact - science • Genuineecological inspiration for distributed adaptation • Continuousevolution and approximatecorrectness

  14. Expected impact - society • Software-intensive, collaborative systems are pervasive in our society • DIVERSIFY aimsatexperimenting in smart cities • Greaterrobustness of otherforms of CAS • assisted living, emergency systems, etc.

  15. Impact, management and dissemination

  16. Management structure

  17. Budget

  18. Efforts

  19. IP management • Foregroundwillbedisseminated in open source • Details about background willbespecified in consortium agreement • CA isbased on DESCA

  20. Infrastructure for collaboration • Social source code and document repository • Private and public github repositories • Shared Folder (SparkleShare) • Private and public wiki • Meeting White board (etherpad) • Announcement (twitter) • Website (drupal) • Visio conference (INRIA visioconference bridge)

  21. Work plan

  22. WP1 Ecological modeling • Objectitves • ensure knowledge transfer from ecologists • formalize and validate software diversity models • formalize and validate software distributed adaptation mechanisms • establish a tight connection with WP2 and WP3 through the collection of state-of-the-art models of biodiversity and distributed adaptation

  23. WP2 • Objectives: • models of software diversity in CASs • synthesize diversity. • lifecycle of diversity

  24. WP3 Objectives and organization • SoTA: self-* Systems • Objectives • Capture Application Needs • Discover/Monitor Diversity • Trigger Application Adaptations Application 1 Application 2 Application N T.3.2 Diversity-based Adaptation T3.4 Diversity-Driven Adaptation Diversity Model (model at runtime) WP2 WP4 T3.3 Monitoring WP1 Environment with diversity Diversity WP2

  25. Work Package 4 WP1: Ecological Modeling WP2&3: Software diversity and distributed adaptation Reference for scenarios Provides evaluation criteria Application and feedback T1: Domain analysis and scenario design T2: Simulation and experiment T3: Evaluation and report D4.1: Scenario design and system investigation D4.4: Experiment report D4.2: Smart City Simulator D4.3: Simulator document and analysis

  26. WP5 Dissemination, collaboration and exploitation • Main objectives • ensure collaboration inside the project • disseminateresultsoutside the project • communicate on the program and particate in the FOCAS CA • 3 tasks: • Infrastructure and support for projectcommunication • Scientificdissemination and exploitation • Collaboration

  27. WP6 Management • Will assure: • global quality; • timely (and in respect with the budget) finalization of the deliverables and reports; and • good communication, collaboration and transparency between the partners and towards the European Commission.

  28. Contributions to SoTA

  29. Background and positioning

  30. Software diversity • The main objective of DIVERSIFY is to develop mechanisms that introduce diversity at runtime, in association with the mechanisms that select the relevant level of diversity according to environmental conditions.

  31. Diversify & Autonomic Computing • Autonomic Computing • Adjusting the system to its environment • How to prune the search space? • DIVERSIFY • Adjusting the environment to the system • Diversified search space => Easy to find a good-enough solution Low probability to find a good-enough solution Reaction time Time needed to find a solution Diversify Higher probability to find a good-enough solution Degree of Diversity Probability to find solutions

  32. ThingML language • Modelling language for the IoT • Based on well established formalisms • Architecture models • Asynchronous messaging • State machines • Imperative action language • Targets the whole spectrum of devices of the Future internet (from microcontrollers to cloud) • A good candidate language for experimenting with diversity • Open-source and available at http://www.thingml.org

  33. ThingML as a bridge between IoT and IoS

  34. Smart City Research at TCD Water manage-ment Urban traffic control Community energy management City watch ... Dynamic optimization of urban resources Dependability, trustworthy, privacy… Data brokerage and simulation ... In-house devices In-vehicle systems Smart phones CCTV

  35. On-going projects • model driven development and formal methods Runtime models to support the adaptation of urban scale systems self-organising of electrical devices on the smart grid for collaborative and coordinated smart vehicle applications Simulation on mainstream grid simulator, GridLAB-D Started from the domain analysis of water distribution systems Integrated simulation environment on vehicles and traffics • Formalisation of • distributed coordination Language-based framework for runtime models LAMP Multi-agent, single policy DWL benchmarked trustworthy participatory & opportunistic sensing Collecting & disseminating sensor data Dynamic adaptation Sensor data processing and city environment simulation services on urban resources Runtime models DYSARM Personal Cities Constraint-driven self-adaptation with user preference MDDSV Use case, CityWatch with Intel

  36. Kevoree in nutshell 1/2

  37. Kevoree in nutshell 2/2

  38. The clonal plant model • More than 70% are clonal withparticular network forms • These network forms are constitued of twounitswithdifferentfunctions Resource acquisition Ramets Connexions Information transfer, storage One clonal network

  39. The clonal plant model • In heterogeneousenvironments, apparition of diversitywithin the network Lowresourceenvironments Ramet specialization (diversification of functions) heterogeneousenvironments • Importance of heterogeneity grain, environmentpredictability, patch contrasts

  40. The clonal plant model • Twoway for diversitydevelopment: • spontaneous(agedependent) • responseto environmentalchanges Local response (growth, (reproduction)) Local environmentalcues (change in environmental conditions, stress, local disturbance) Scale of signal integration Treshold for responsedevelopment (trade-off cost vs. theoreticalbenefitat the network level) Global network performance (efficiency in resource acquisition (-> biomass), network survival)

  41. Relation with close projects

  42. Relation with PerAda and Awareness • Common • Focused on the self-awareness and self-adaptation of software-based systems • Started from large data, services, and learning-based technologies • Share some common topics (such as e-Mobility with ACENS, a project under Awareness) • Difference • We focus more on the urban infrastructure (water, energy), rather than the social aspects • We focus more on software (services), rather than control (robots) • We are from a software engineering perspective, utilizing MDE, middleware technology, etc.

  43. Related projects - AWARENESS 1/5 • Sapere = Self-aware Pervasive Service Ecosystems • Model and deploy services as autonomous individuals in an ecosystem of other services, data sources, and pervasive devices. • Self-aware components and a general nature-inspired interaction model • Decentralized self-* algorithms • Spatial self-organization, self-composition, and self-management • Diversify will takes inspiration from Ecology by involving Ecologists in the project, and will mainly focus on leveraging the diversity and food web properties from Ecosystems to build reliable systems

  44. Related projects - AWARENESS 2/5 • Cocoro = Collective Cognitive Robots • Swarm intelligence inspire from natural and biology phenomenon • Application to: robots, underwater vehicles ... • Diversify will not focus on this type problems.

  45. Related projects - AWARENESS 3/5 • ASCENS : Autonomic Service-Component ensembles • Combine formal method and optimal resource usage promised by autonomic computing • Apply to robotics, cloud computing and e-Vehicles • Diversify has a totally different approach build self-adaptive resilient applications inspired by eco-systems • EPICS : Engineering Proprioception in Computing Systems • Proprioception (coming from psychology) is the basic ability to collect and maintain information about state and progress • Transfer knowledge from another science to computer science • Diversify follow the same process for transferring knowledge from another science to computer science, but will focus on transferring knowledge from Ecology and will integrate Ecologist as core partners of the project

  46. Recognition: Relevance and cognition for self-awareness in a content-centric Internet inspired by the cognitive process of human using psychological and cognitive science apply to Internet content Diversify is not doing the same thing ... Related projects - AWARENESS 4/5

  47. SYMBRION : Symbiotic Evolutionary Robot Organisms swarm & collective robot systems - evolutionary robot organisms apply to flots of robots Symbrion is cited in PerADA and Awareness A bit particular because the website speaks about 3 projects (one in Awareness and the other in PerADA) + Symbrion Enlarged EU + un projet REPLICATOR Related projects - AWARENESS 5/5

  48. ALLOW : Adaptable Pervasive Flows New programming paradigm for developing adaptable pervasive flows Compared to Diversify : Use traditionnal techniques of Context-aware programming and so on. Related Project - PerAda 1/5

  49. ATRACO : Adaptive and Trusted Ambient Ecologies A context-aware artefact, appliance or device uses sensors to perceive its context of operation and applies an ontology to interpret this context. It also uses internal trust models and fuzzy decision making mechanisms to adapt its operation to changing context. Diversify will works with Ecologist to really transfer knowledge from Ecology to computer science. Related Project - PerAda 2/5

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