When building your IoT solution you face different steps. They normally include Device registration, Data Ingestion, Data processing and Data analysis. So come hear how to model this process on micro services architecture and then hosting whole thing on premise or on a cloud with Azure Service Fabric.

1. Onsight
Building IoT solution with
Service Fabric

2. Onsight
Teemu Tapanila
 Microsoft Azure MVP
 Architect, OnSight Helsinki oy
 Microsoft Certified Trainer
Teemu@onsight.fi
@TapanilaT

3. Onsight
Topics
 Why Azure Service Fabric
 Device Registration
 Data Ingestion
 Data processing
 Data analysis

4. Onsight
Microsoft Azure Service Fabric
A platform for reliable, hyperscale, microservice-based applications
Microservices
Azure
Windows
Server
Linux
Hosted Clouds
Windows
Server
Linux
Service Fabric
Private Clouds
Windows
Server
Linux
High Availability
Hyper-Scale
Hybrid Operations
High Density Rolling Upgrades
Stateful services
Low Latency
Fast startup &
shutdown
Container Orchestration
& lifecycle management
Replication &
Failover
Simple
programming
models
Load balancing
Self-healingData Partitioning
Automated Rollback
Health
Monitoring
Placement
Constraints

5. Onsight
Battle-hardened for over 5 years

6. Onsight
What is a microservice?
 Is (logic + state) that is independently versioned, deployed, and
scaled
 Has a unique name that can be resolved
 e.g. fabric:/myapplication/myservice
 Interacts with other microservices over well defined interfaces and
protocols like REST
 Remains always logically consistent in the presence of failures
 Hosted inside a “container” (code + config)
 Can be written in any language and framework
 Developed by a small engineering team

7. Onsight
Types of microservices
 Stateless microservice
 Has either no state or it can be retrieved from an external store
 There can be N instances
 e.g. web frontends, protocol gateways etc.
 Stateful microservice
 Maintain hard, authoritative state
 N consistent copies achieved through replication and local
persistence
 e.g. database, documents, workflow, user profile, shopping cart
etc.

10. Node
Node
Node
Node
Node
Node

11. Time =
t1
83
76 50
46
64 New Node arrived61
Time =
t2
83
61
50
46
Failures
Detected
cluster reconfigured
83
76
64
50
46
Time =
t0
Nodes failed

12. System Services
Failover
manager
Cluster
manager
Naming
Image
store

14. Queues Storage
3-Tier service pattern
Front End
(Stateless
Web)
Stateless
Middle-tier
Compute
Cache





Load Balancer

15. Stateful
Middle-tier
Compute
Stateful services: Simplify design, reduce latency
Front End
(Stateless
Web)
data stores used for analytics and disaster recovery





Load Balancer

16. Onsight
Device Registration
 Device needs to be registered when it gets connected to
the system.
 This includes normally authentication keys and serial
number
 When device gets registered you should assing it value
which you will later use for sharding

17. Onsight
Data Ingestion
 Gathered data from 1 data source is not so important
normally
 Real-time data ingestion vs batch ingestion

18. Onsight
Data Ingestion

19. Onsight
Data processing
 Visibility to data averages on grouping level (Machine,
Building, Floor or etc)
 Logic based on value changes

20. Stateful
Middle-tier
Compute
Data ingestion & Processing
Front End
(Stateless
Web)
data stores used for analytics and disaster recovery
Load Balancer

21. Onsight
Data processing
Room Stateful Service
Floor Stateful Service
Building Stateful Service
Campus Stateful Service

22. Onsight
Data Analysis
 Data is stored outside of service fabric for processing
 Most cases makes sense of using other tools to build the
analysis platform than Service Fabric

23. Microsoft Azure Service Fabric
A platform for reliable, hyperscale, microservice-based applications
Microservices
Azure
Windows
Server
Linux
Hosted Clouds
Windows
Server
Linux
Service Fabric
Private Clouds
Windows
Server
Linux
High Availability
Hyper-Scale
Hybrid Operations
High Density Rolling Upgrades
Stateful services
Low Latency
Fast startup &
shutdown
Container Orchestration
& lifecycle management
Replication &
Failover
Simple
programming
models
Load balancing
Self-healingData Partitioning
Automated Rollback
Health
Monitoring
Placement
Constraints

24. Onsight
Thank you!
Teemu@onsight.fi
@TapanilaT