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.

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
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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
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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.
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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. Motivation (1)
 Application scenarios: emergency responses, on-demand
crowd sensing, Geo Sports monitoring, cyber-physical
systems testing, etc.
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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. 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
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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
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End-to end
Resource slice
Applications/Virtual
infrastructures

7. SINC conceptual framework
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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)
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IoT networks Network Function Services Clouds
Resource Grid

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
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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
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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
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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
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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/
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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
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15. Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/staff/truong
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