Modeling and Provisioning IoT Cloud Systems for Testing Uncertainties

Modeling and Provisioning IoT Cloud
Systems for Testing Uncertainties
Hong-Linh Truong, Luca Berardinelli, Ivan Pavkovic,
Georgiana Copil
Faculty of Informatics, TU Wien
hong-linh.truong@tuwien.ac.at
http://rdsea.github.io
MobiQuitous 2017, 8 Nov 2017. Melbourne 1

Outline
 Motivation
 Tool Pipeline
 Modelling Uncertainties
 Provisioning for Uncertainty Testing
 Prototype and Examples
 Conclusions and Future work

Motivation applications
 Various sensors/IoT systems and analytics with different
cloud deployment environments
 Elastic workload (during the games/practices)
MobiQuitous 2017, 8 Nov 2017.
Melbourne
3
Geo Sports: Picture courtesy
Future Position X, Sweden

Public cloud infrastructures
Private cloud infrs.
Base Transceiver Station (BTS)
Motivation applications: Base
Transceiver Station monitoring
 Large-scale systems (1K+ BTS)
 Flexible back-end clouds
 Generic enough for other applications (e.g., in smart agriculture)
 With bad infrastructures for IoT and connectivity
Sensor
IoT
Gateway
MQTT
Broker
BigQuery
Influxdb
Hadoop FS
G. StorageActuator
Optimizer Analytics
Analytics
Analytics

Complex systems with many
configuration possibilities
 Complexity of the underlying virtual computing and
network infrastructures
 Heavily based on virtual elastic resources
 Require vast knowledge for software development and
operation
IoT Cloud Systems
The SINC Concept:
http://sincconcept.github.io

Uncertainty Concepts for CPS
from H2020 U-Test
In U-Test: uncertainty means here the lack of certainty
(i.e., knowledge) about the timing and nature of inputs,
the state of a system, a future outcome, as well as other
relevant factors.
WP1: Uncertainty Taxonomy, Use
Cases, and Evaluation Plans
Understanding Uncertainty in Cyber-
Physical Systems (
https://www.simula.no/file/d12pdf/dow
nload
D1.2)
www.u-test.eu
source

Uncertainties in the development
and operations
 Huge lack of knowledge
 Due to complexity of the IoT, cloud services software
stacks
 Many uncertainties are also due to runtime operations of
virtual infrastructures
 There is a lack of work to quantify uncertainty
 Supporting testing uncertainties and uncertainties
analytics
 Emerging novel aspects: data uncertainties (data/data-
centric IoT Cloud systems), elasticity of IoT cloud resources
(w.r.t function and composition), and Governance (related
to business/trustworthiness)

Data
ElasticityGovernance
Important IoT Cloud uncertainties
classes
Storage
Uncertainty
Infrastructure
Uncertainty
DataDelivery
Uncertainty
Actuation
Uncertainty
DeploymentTime
Uncertainty
ExecutionEnvironment
Uncertainty
Storage
ComplianceUncertainty
StorageQuality
Uncertainty
StorageDependability
Uncertainty
DataQuality
Uncertainty
DataDelivery
DataDeliveryDependability
Uncertainty
ActuationDependability
Uncertainty
EnvironmentDependability
Uncertainty
Actuation
ApplicationDependability
Uncertainty
Runtime
Uncertainty

Tool Pipeline
 For testing of IoT Cloud
Systems
 Dealing with multiple types
of artifacts
 Public artifacts about elements
of IoT Cloud Systems from
existing providers
 Customized artifacts of
elements in system under test
(SUT)
 Test utilities: real tests need
different utilities to deal with
different resource instances
Virtual Machine
ProviderA
Sensor - Container
Log
Collector
Log Collector
Virtual machine
artifact
Sensor - Docker
container artifact
Log collector
artifact
deploy

Modeling, Provisioning and Testing
IoT Cloud Infrastructures
Modeling
Uncertainties
and SUT
Uncertainty
Profile
System Under
Test (SUT)
Models
Generating
Uncertainty
Test cases
Deploying/
Configuring
SUT
System Under
Test (SUT)
Test cases
Executing
Tests
IoT/Cloud
Resource
Information
Deploying/
Configuring
Testing Utilities
Test
Utilities
Hong-Linh Truong, Luca Berardinelli, Testing Uncertainty of Cyber-physical Systems in IoT Cloud Infrastructures – Combining
Model-Driven Engineering and Elastic Execution, Workshop on Testing Embedded and Cyber-Physical Systems, ISSTA
2017, Santa Barbara, 10-14 July, 2017.

Tool Pipeline
Key focus: providing fundamentals for modeling,
information extraction, configuration generation and
provisioning to enable testing
Extracting model
information &
Generating test
configuration
test
strategies
Infrastructural
IoT Cloud
resources
Provisioning &
Configuring SUT
Executing Tests
Test plans &
executors
IoT Units
CloudT
E
S
T
S
ProviderA
ActuatorsSensors
VMsAnalytics GWs
IoT Units
CloudT
E
S
T
S
ProviderB
ActuatorsSensors
VMsAnalytics GWs
Infrastructure
(Class
Diagrams)
Behaviour
(State
Machine
diagrams)
Uncertainty

Modeling: IoT and Cloud Resource
Profile

Uncertainty Modeling: Uncertainty
Profile
 Uncertainties have many different categories
 There are also different levels of modeling, e.g., only
the type or the type and its detailed measurement

Modeling of Generic models for
Task Executor
 Correlate
measurement
metrics and
uncertainty
 Tasks for testing
also including
deployment and
reconfiguration of
ensembles.

Provisioning: Extracting models
We need an intermediate information for many
purposes: using JSON

Provisioning: Artifact repository
and runtime service
 Several types of artifacts are provided by
different stakeholders
 we consider that artifacts for testing (public artifact,
system under tests and test utilities) are available
 Both static and runtime services are needed
 Static: for storing deployable artifacts, e.g., using GIT,
docker repository and cloud software repository
 Runtime: for knowing what have been already
deployed
 We use HINC (http://sincconcept.github.io/HINC/) and cloud
runtime monitoring systems

Provisioning: Selected SUT related
artifacts
 Selected SUT related artifact follow convention (no
standard exists) – best practice in clouds and software-
defined IoT
 Metadata about abstract/concrete components for
testing and tags
 Meta information about how to invoke, e.g. startup,
shutdown, configuration of instances

Configuration Generation and
Deployment
 Depending on strategies, available artifacts and cloud
services we generate different configuration
 Reuse well-known tools for IoT Cloud deployment
 Deployment is only when needed
 Elasticity in testing: Interwoven testing + deployment

Prototype
 Initial prototype for modeling
 T4UME: https://github.com/rdsea/T4UME
 Also incorporated into U-Certify
 Some system under test (SUT)
 https://github.com/rdsea/IoTCloudSamples
 Other SUTs are project confidential
 Provisioning and test prototype will be released
later at

Example: Modeling the BTS
Note: An extracted prototype from the confidential one is available at: https://github.com/rdsea/IoTCloudSamples
Many possible configurations

Example of Uncertainties w.r.t data
 First approach: find and detect if missing information
during the development
 Second approach: generate different possibilities and
test them with elasticity constraints from the user

Examples of configuration
generation
"MQTTConfig1": {
"name": "MQTTConfigServer",
"protocolType": "MQTT",
"qosLevel": [],
"type": "CommunicationConfiguration"
}
"MQTTConfig2": {
"name": "MQTTConfigClient",
"protocolType": "MQTT",
"clientID": "",
"serverIP": "35.189.187.208",
"portNumber": 1883,
"topics": ["/gateway/electricity"],
"qosLevel": [2],
"type": "CommunicationConfiguration"
}
services:
ingest:
build: .
volumes:
- ./:/t4u
electricitysensor:
image:
"localhost:5000/t4u/mqttsensor/realsensor:
v01"
iotgateway:
image:
"localhost:5000/t4u/cloudservice/mqttbroke
r:v01"
Enriched model information for
deployment configurations Generated deployment description

Elasticity in Testing Uncertainties
MobiQuitous 2017, 8 Nov 2017.
Melbourne
23
" BTSBroker1Local ": {
" name ": " BTSBroker ",
" cloudProvider ": [" local "],
" communicationConfigs ": [" MQTTConfig1 "],
" type ": " CloudService "
}
" MQTTConfig1 ": {
" name ": " MQTTConfigServer ",
" protocolType ": " MQTT ",
" qosLevel ": [],
" keepAlive ": 210 ,
" type ": " CommunicationConfiguration "
}
" BTSBroker2Google ": {
" name ": " MQTTBroker ",
" cloudProvider ": [" Google "],
" communicationConfigs ": [" MQTTConfig1 "],
" type ": " CloudService "
}

Elasticity in Testing Uncertainties
" MQTTConfig2 ": {
" name ": " MQTTConfigClient ",
" clientID ": "",
" serverIP ": "35.189.187.208" ,
" portNumber ": 1883 ,
" topics ": ["/ gateway / electricity "],
" qosLevel ": [2] ,
}
" ElectricitySensor1 ":{
" name ": " ElectricitySensor ",
" swCapabilities ": [" setRate ", " getRate "],
" ownedUnits ": [" HwElectricitySensor "],
" type ": " VirtualSensor "
}
" CommElectricitySensorIoTGateway1 ": {
" name ": " CommElectricitySensorIoTGateway ",
" connection_end_points ": ["
ElectricitySensor1 ", "
IoTGateway1Google "],
" communicationConfigs ":" MQTTConfig2 ",
" type ": " Communication "
}
Only obtained from
runtime (after
deploying the MQTT
broker for testing)
" MQTTConfig3 ": {
" name ": " MQTTConfigClient ",
" clientID ": "",
" serverIP ": “localhost" ,
" portNumber ": 1883 ,
" topics ": ["/ gateway / electricity "],
" qosLevel ": [1] ,
}
runtime change

Summary and Future work
 Modeling and testing uncertainties are very
challenging in IoT, network functions and clouds
 Testing uncertainties require fundamental changes in testing
techniques: deal with elasticity and virtualization
 Our tool layouts fundamental steps for elastic testing of
uncertainties
 Check http://github.com/rdsea
 Future work
 Integration with software testing engines /MBT testing tools
 Algorithms for configuration generation
 Consider more data and network uncertainty aspects.
 Adaptation and optimization under uncertainties

Thanks for your
attention!
Hong-Linh Truong
Faculty of Informatics
TU Wien
rdsea.github.io

Modeling and Provisioning IoT Cloud Systems for Testing Uncertainties

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