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HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds

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Effective resource management in IoT systems must
represent IoT resources, edge-to-cloud network capabilities, and
cloud resources at a high-level, while being able to link to diverse
low-level types of IoT devices, network functions, and cloud
computing infrastructures. Hence resource management in such
a context demands a highly distributed and extensible approach,
which allows us to integrate and provision IoT, network functions,
and cloud resources from various providers. In this paper, we
address this crucial research issue. We first present a high-
level information model for virtualized IoT, network functions
and cloud resource modeling, which also incorporates software-
defined gateways, network slicing and data centers. This model
is used to glue various low-level resource models from different
types of infrastructures in a distributed manner to capture
sets of resources spanning across different sub-networks. We
then develop a set of utilities and a middleware to support
the integration of information about distributed resources from
various sources. We present a proof of concept prototype with
various experiments to illustrate how various tasks in IoT cloud
systems can be simplified as well as to evaluate the performance
of our framework.

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HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds

  1. 1. HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds Duc-HungLe, Nanjangud Narendra, Hong-Linh Truong Distributed Systems Group, TU Wien truong@dsg.tuwien.ac.at http://dsg.tuwien.ac.at/staff/truong FiCloud 2016, Vienna, 24th August 2016 1
  2. 2. Outline  Background and motivation  HINC framework  Distributed resource information model  Architecture and implementation  Testbed and experiments FiCloud 2016, Vienna, 24th August 2016 2
  3. 3. Background - Systems 3 Analysis & management Hot deploy Control Re-route FiCloud 2016, Vienna, 24th August 2016
  4. 4. Background – elastic service models  Cloud service models  Networks  Network function virtualization  Pay-per-use IoT communication  IoT  Fixed IoT infrastructures  On-demand IoT  Human participation (sensing and analytics) FiCloud 2016, Vienna, 24th August 2016 4 https://arrayofthings.github.io/ http://www.sktelecom.com/en/press/detail.do?idx=1172
  5. 5. Background - application scenarios  Emergency responses, on-demand crowd sensing, Geo Sports monitoring, cyber-physical systems testing, etc. FiCloud 2016, Vienna, 24th August 2016 5  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. Geo Sports: Picture courtesy Future Position X, SwedenIndian Overfly collapses figure source: http://timesofindia.indiatimes.com
  6. 6. Motivation – End-to-End resource slice provision 6 Emergency response Hospital & traffic Emergency response service Early treatment protocol Best route to the hospital Most suitable hospital - Wearables - Mobie medical equipment - First aid info. - Vehicle capability - Location - Hospital capability - Traffic status Victims Distributed resource management IoT resource provisioning Dedicate sub-network Coordinate operations FiCloud 2016, Vienna, 24th August 2016
  7. 7. Motivation – End-to-End resource slice provisioning FiCloud 2016, Vienna, 24th August 2016 7 End-to end Resource slice CPS Applications/Virtual infrastructures http://sincconcept.github.io/ This paper: harmonize resource information from IoT, network functions and cloud providers for resource slice provisioning
  8. 8. Examples of existing providers/models Provider Category APIs Information models FIWare Orion IoT RESTful (NGSI10), one-time query or subscription High level attributes on data and context FIWare IDAS IoT RESTful for read/write custom models and assets Low level resource model catalogs IoTivity IoT REST-like OIC protocol, support C++, Java and JavaScript Multiple OIC model OpenHAB IoT RESTful for query and control IoT resources Low level resource model catalogs OpenDayLight Network Dynamic REST generated from Yang model (model-driven) Low level resource model catalogs OpenBaton Network RESTful for network service description ETSI MANO v1.1.1 data model OpenStack Cloud RESTful, multiple language via SDK, OCCI, CIMI OpenStack model, OCCI, CIMI 8 FiCloud 2016, Vienna, 24th August 2016
  9. 9. Approach 9 FiCloud 2016, Vienna, 24th August 2016 • Avoid top-down • Design a “super” model to manage the world. • Focus and suitable for single-purpose solution. • Bottom-up • Let providers use their own models. • Integration and link diverse types of information. • Adaptor: to interface with providers’ APIs. • Transformer: integrate our distributed resource model. • Focus on resource relationships across IoT, Network functions and clouds.
  10. 10. Information model Physical: Sensor/actualtor/devices in providers’ models Virtual IoT: SD-Gateway and capabilities. Network functions: edge-to-edge, edge-to-cloud network. Clouds: VM, data services, data analytics. 10 FiCloud 2016, Vienna, 24th August 2016
  11. 11. Resource information integration • The model aims to be extensible to cater multiple underlying devices and services. • To cope with the rapidly increasing of systems. • A process to interface with resource providers. 11 FiCloud 2016, Vienna, 24th August 2016
  12. 12. Examples FiCloud 2016, Vienna, 24th August 2016 12 "data": { "DeviceProps": { "commandURL": "http://...OpenIoT/..", "lastIP": "195.97.103.225", "commands": true }, "asset": { "name": "00:3b:B6:BodyTemperature", "description": "asset model protocol" }, "model": "SENSOR_TEMP", "registrationTime": "2015-04-16T15:39:58Z", "status": "Active", "sensorMetaData": [ {"ms": { "dataType": "BodyTemperature", "unit": "Celsius", "rate": "10" } }]} { "type": "LocationItem", "link": "http://..../rest/items/DemoLocation" } Resource from OpenHAB - Simple data format. - A link for more information. - Information is static. - Complex data format. - Have control capability. - More meta data. A resource from OpenIOT
  13. 13. Examples FiCloud 2016, Vienna, 24th August 2016 13 "SoftwareDefinedGateway":{ "uuid": "5a60...", "name": "gateway1", “datapoints": [ { “name": “Temp1", "datatype": "BodyTemerature", "measurementUnit": "Celsius", "resourceID": "00:3b:B6:BodyTemperature", "extra": [ <imported List 1 and List 2> ...} ], }, “controlpoints”: [ { "name": "changeRate", "resourceID": "00:3b:B6:BodyTemperature", "description": "change sensor rate", "reference":"http://.../OpenIoT/assets/..", } ], } Virtual IoT resource information - Software-Defined Gateway wraps a set of capabilities. - Data Point extracts a set of interesting attributes for higher level management. - Control Point contains a reference to the provider API for controlling resources.
  14. 14. Architecture 14 Global management service - Run on users’ site. - Coordinate Local Management Service. - Manage relationships. Local management service - Deployed on gateway or network station. - Interface with provider. - Transform information. FiCloud 2016, Vienna, 24th August 2016
  15. 15. Prototype 15 FiCloud 2016, Vienna, 24th August 2016 http://sincconcept.github.io/HINC/
  16. 16. Testbed 16 FiCloud 2016, Vienna, 24th August 2016
  17. 17. Testbed 17 FiCloud 2016, Vienna, 24th August 2016 In-lab testbed: - Server: 8 CPU-i7, 3.60GHz, 32GB RAM - Edge: 100 dockers with emulated sensors + gateways. - Network: virtual routers (https://www.weave.works/) - Cloud: event processing.
  18. 18. Testbed 18 FiCloud 2016, Vienna, 24th August 2016 Distributed testbed - Edge: physical/virtual machines on different cities. - Communication: CloudAMQP. - Cloud: AmazonEC.
  19. 19. Reducing complexity in accessing and control resources 19 FiCloud 2016, Vienna, 24th August 2016 1. Query data points 2. Control the resource 3. Query network functions and clouds
  20. 20. Query time by number of gateways Distributed sites testbed FiCloud 2016, Vienna, 24th August 2016 20 In-lab testbed
  21. 21. Gateway’s response time variability FiCloud 2016, Vienna, 24th August 2016 21 Distributed sites testbed In-lab testbed
  22. 22. Number of sensors and locations FiCloud 2016, Vienna, 24th August 2016 22 Query time from distributed sites Query time by number of sensors, distributed sites
  23. 23. Conclusion and future work  Harmonizing information in 3 dimensions:  High-level view of low level resources  End-to-end view of IoT, network functions and clouds  Large-scale view of highly distributed sites  Future work:  Information-centric resource provisioning  Dynamic IoT infrastructure configuration  End-to-end resource optimization 23 FiCloud 2016, Vienna, 24th August 2016
  24. 24. Thanks for your attention! Questions? Hong-Linh Truong Distributed Systems Group TU Wien dsg.tuwien.ac.at/staff/truong FiCloud 2016, Vienna, 24th August 2016 24

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