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SINC – An Information-Centric Approach
for End-to-End IoT Cloud Resource
Provisioning
Hong-Linh Truong and Nanjangud Naren...
Outline
 Motivation
 Challenges
 The SINC conceptual framework
 Overall architecture
 API management and integration
...
State-of-the art IoT Clouds/Cyber-
Physical Systems
 Complex infrastructures of IoT elements (sensors,
gateways, networks...
Motivation (1)
 Application scenarios: emergency responses, on-demand
crowd sensing, Geo Sports monitoring, cyber-physica...
Motivation (2)
 Problems
 Virtual resources are provided by different providers
 Often there is no coordination among t...
Challenges
 Modeling distributed IoT, network functions and cloud
capabilities in an integrated view
 Slicing end-to-end...
SINC conceptual framework
ICCCRI2016@CloudAsia2016, 4th May 2016 7
Integrating diverse types of resources
 Make a Resource Grid ready for slice creation
 How to harmonize and gather IoT, ...
Naming, Slicing and Routing
 From Resource Grid to information-centric description
of Partitions of Resources for slices
...
Resource Management,
Configuration and Adaptation (1)
 Creating slices, each slice includes a set of partitions of
resour...
Resource Management,
Configuration and Adaptation (2)
 Monitoring and Management
 Develop end to end metrics for slices
...
Towards the Implementation
 Using REST API to integrate resource management
capabilities from different providers
 Distr...
Towards the Implementation - HINC
 Implement API Integration and
Communication
 http://sincconcept.github.io/HINC/
 Hig...
Conclusions and Outlook
 Slicing IoT, network functions and clouds
 Important for various types of applications
 Key to...
Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/staff/truong
ICC...
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SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Provisioning

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We present SINC –
Slicing IoT, Network Functions, and Clouds – which enables designers to dynamically create/update end-to-end slices of the overall IoT network in order to simultaneously meet multiple user needs.

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SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Provisioning

  1. 1. SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Provisioning Hong-Linh Truong and Nanjangud Narenda Distributed Systems Group, TU Wien truong@dsg.tuwien.ac.at dsg.tuwien.ac.at/staff/truong Ericsson Research, Bangalore, India nanjangud.narendra@ericsson.com ICCCRI2016@CloudAsia2016, 4th May 2016 1
  2. 2. Outline  Motivation  Challenges  The SINC conceptual framework  Overall architecture  API management and integration  Naming, slicing, and routing  Slice management and adaptation  Towards the implementation of SINC  Conclusions and future work ICCCRI2016@CloudAsia2016, 4th May 2016 2
  3. 3. State-of-the art IoT Clouds/Cyber- Physical Systems  Complex infrastructures of IoT elements (sensors, gateways, networks), micro data centers, network services, cloud VM, storage, etc. ICCCRI2016@CloudAsia2016, 4th May 2016 3 Cloud (big, centralized data centers) Cloud (big, centralized data centers) Edge (IoT devices, micro data centers) Edge (IoT devices, micro data centers) Edge (IoT devices, micro data centers) Edge (IoT devices, micro data centers) Network functions (network services + micro datacenter  On-demand resources provisioning across IoT networks (the edge), network functions (the middle) and the clouds (the back-end)
  4. 4. Motivation (1)  Application scenarios: emergency responses, on-demand crowd sensing, Geo Sports monitoring, cyber-physical systems testing, etc. ICCCRI2016@CloudAsia2016, 4th May 2016 4 Geo Sports: Picture courtesy Future Position X, Sweden Indian Overfly collapses figure source: http://timesofindia.indiatimes.com  Need to have an end-to-end provisioning of resources  E.g., sensors, network function services, storage, virtual machines  Short, crucial and heavily workload; elasticity and uncertainties.
  5. 5. Motivation (2)  Problems  Virtual resources are provided by different providers  Often there is no coordination among them  inadequate support for elasticity and uncertainties for the application  Host-centric information is too low level to represent “slice” view  It is very hard, if not impossible, to establish end-to end view on resources  lack of tools, too complex, time-consuming, & error- prone effort for application users and developers  Our contribution  A conceptual framework for slicing IoT, network functions and cloud resources ICCCRI2016@CloudAsia2016, 4th May 2016 5
  6. 6. Challenges  Modeling distributed IoT, network functions and cloud capabilities in an integrated view  Slicing end-to-end network of resources  Composing resources in slices of IoT, network functions and clouds  (Re-)configuring composed resources ICCCRI2016@CloudAsia2016, 4th May 2016 6 End-to end Resource slice Applications/Virtual infrastructures
  7. 7. SINC conceptual framework ICCCRI2016@CloudAsia2016, 4th May 2016 7
  8. 8. Integrating diverse types of resources  Make a Resource Grid ready for slice creation  How to harmonize and gather IoT, network functions and cloud resources  API Integration and Communication  Use REST API for obtaining metadata and control of resources  Sensoring data can be transferred through different middleware  Work with existing metamodel (IoTivity, OpenHAB, IoTDM, ETSI MANO, OCCI, CIMI, etc.)  Rely on scalable cloud middleware (e.g., AMQP & MQTT) ICCCRI2016@CloudAsia2016, 4th May 2016 8 IoT networks Network Function Services Clouds Resource Grid
  9. 9. Naming, Slicing and Routing  From Resource Grid to information-centric description of Partitions of Resources for slices  Information-centric description of resources from IoT, network and clouds; modeling partitions of resources  Slicing  Leveraging network slicing techniques (e.g., 5G)  Leveraging IoT and cloud virtualization to provision on-demand dedicated resources with elasticity capabilities  Routing  Utilize concepts of Forwarding Information Base (FIB) and Pending Interest Table (PIT) for routing control commands and data queries to underlying resources  Separate control commands and data queries from sensoring data transportation ICCCRI2016@CloudAsia2016, 4th May 2016 9
  10. 10. Resource Management, Configuration and Adaptation (1)  Creating slices, each slice includes a set of partitions of resources  Modeling and capturing user requirements for slices  Creation and Management  Develop new algorithms for creating slices by leveraging existing works for IoT, networks, and services  Integrate with NFV orchestrators, virtual sensors, gateways, cloud APIs and SDN controllers.  Deal with different resource provisioning models imposed by underlying infrastructures  Configuration by leveraging different deployment tools for IoT, network functions and clouds ICCCRI2016@CloudAsia2016, 4th May 2016 10
  11. 11. Resource Management, Configuration and Adaptation (2)  Monitoring and Management  Develop end to end metrics for slices  Integrate monitoring capabilities from different providers and correlating monitoring data  Runtime slice adaptation  Performance as well as uncertainties at infrastructures, applications and their integration levels  Adaptation capabilities across IoT, network functions and clouds  Multiple level of adaptations based on end-to-end problems and partition problems ICCCRI2016@CloudAsia2016, 4th May 2016 11
  12. 12. Towards the Implementation  Using REST API to integrate resource management capabilities from different providers  Distributed communication middleware, e.g., based on AMQP/MQTT, for querying resource information and propaging controls  TOSCA or other topology description tools for modeling topologies for supporting configuration and deployment  Leveraging existing deployment techniques for IoT and clouds  Testbed established with open sources: Dockers, OpenStack, Weave, OpenDayLight, etc. by utilizing cloud, network and IoT devices ICCCRI2016@CloudAsia2016, 4th May 2016 12
  13. 13. Towards the Implementation - HINC  Implement API Integration and Communication  http://sincconcept.github.io/HINC/  High level information models for Resource Grid  Middleware and adaptors for integrating different providers  API for querying and configuring resources  Leveraging SALSA for IoT, network functions and cloud configuration  http://tuwiendsg.github.io/SALSA/ ICCCRI2016@CloudAsia2016, 4th May 2016 13
  14. 14. Conclusions and Outlook  Slicing IoT, network functions and clouds  Important for various types of applications  Key to the coordination of diverse types of resources in distributed edge and cloud systems  SINC: a conceptual framework and steps to achieving end-to- end resources provisioning  Ongoing work  Slice requirement modeling and composition algorithms  APIs for programming resource queries and controls  Configuration tools (http://tuwiendsg.github.io/SALSA/)  Uncertainty testing and analytics (www.u-test.eu)  Testbed (Vienna, Bangalore, Hanoi, and public clouds) Check http://sincconcept.github.io for new update ICCCRI2016@CloudAsia2016, 4th May 2016 14
  15. 15. Thanks for your attention! Questions? Hong-Linh Truong Distributed Systems Group TU Wien dsg.tuwien.ac.at/staff/truong ICCCRI2016@CloudAsia2016, 4th May 2016 15

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