we introduce U-GovOps – a novel framework for dynamic, on-demand governance of elastic IoT cloud systems under uncertainty. We introduce a declarative policy language to simplify the development of uncertainty- and elasticity-aware governance strategies. Based on that we develop runtime mechanisms, which enable mitigating the uncertainties by monitoring and governing the IoT cloud systems through specified strategies.

1. Governing Elastic IoT Cloud Systems
under Uncertainties
Stefan Nastic, Georgiana Copil, Hong-Linh Truong,
Schahram Dustdar
Distributed Systems Group, TU Wien
truong@dsg.tuwien.ac.at
dsg.tuwien.ac.at/staff/truong
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 1

2. Outline
§ IoT Cloud Systems & Motivation
§ IoT Cloud Uncertainties
§ Specifying uncertainties in governance
processes
§ Actuation under uncertainties
§ Experiments
§ Conclusions and future work
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 2

3. Motivation
§ IoT Cloud Systems/CPS: blending IoT elements and
cloud services for complex applications/services
§ We need to coordinate both IoT elements and cloud
services at the same time
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 3
Hong Linh Truong, Schahram Dustdar: Principles for Engineering IoT Cloud Systems. IEEE Cloud Computing 2(2): 68-76 (2015)
https://github.com/tuwiendsg/COMOT4U/blob/master/models/IoTCloudSystem
http://tuwiendsg.github.io/iCOMOT/

4. Motivation
§ Management and coordination of IoT elements
and cloud services
§ Emerging novel aspects related to infrastructure
data, elasticity control and governance of policies
§ Challenges
§ Which types of uncertainties are in IoT cloud system
infrastructures?
§ Important for infrastructure and state management
§ How to govern IoT cloud systems under such
uncertainties?
§ Which elements should be governed and how to carry out
management operations considering uncertainties?
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 4

5. IoT Cloud
Infrastructure
Uncertainty
Taxonomy
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Infrastructure
uncertainties
Nonfunctional
dimensionality
Functional
dimensionality
Execution
environment
Storage
Data
delivery
Actuation
Elasticity
Governance
Locality
Platform
(virtual
infrastructure
layer)
Hardware
Temporal
manifestation
Persistent
Recurring
Sporadic
Effect
propagation
Application
Physical
environment
External
to
infrastructure
Observation
time
Deployment
time
Runtime
Cause
Human
action
Natural
phenomenon
Quality
Compliance
Dependability
Technological
Human
Composite
Function
Further check:
• https://github.com/tuwiendsg/COMOT4U/blob/master/docs/u-taxonomy.pdf
• www.u-test.eu

6. Uncertainties due to Data Quality
and Actuation Dependability
§ Data needed for governance
§ Status of IoT cloud systems
infrastructure elements: availability,
operational capabilities, etc.
§ Meta-data about infrastructure
elements: location, type of
gateways, owners, etc.
§ Actuation operation: failed, delay, side-
effects
§ DataQualityUncertainties: about
monitoring data/infrastructure state
§ ActuationDependabilityUncertainties
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 6
Governance
DataDelivery
Uncertainty
Infrastructure
Uncertainty
Governance
Uncertainty
GovernanceProcess
ExecutionUncertainty
Actuation
Uncertainty
ExecutionEnvironment
Uncertainty
RuntimeExecution
EnvironmentUncertainty

7. U-Govops architecture
Key contributions: governance policy specification and
governance enforcement considering uncertainties
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 7
Prototype: https://github.com/tuwiendsg/COMOT4U/tree/master/uGovOps

8. SYBL for uGovops
§ SYBL:
§ Directive language for
elasticity requirements
specification
§ Used for elasticity control of
cloud services
§ Extensions for uncertainty
of IoT Cloud Systems:
§ GOVERNANCE_SCOPE
§ CONSIDERING_UNCERTAINTY
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 8
http://dsg.tuwien.ac.at/research/viecom/SYBL/
https://github.com/tuwiendsg/COMOT4U/blob/master/docs/UGovOpsSYBLLanguage.pdf
#SYBL.CloudServiceLevel
Cons1: CONSTRAINT responseTime < 5 ms
Cons2: CONSTRAINT responseTime < 10 ms
WHEN nbOfUsers > 10000
Str1: STRATEGY CASE fulfilled(Cons1) OR
fulfilled(Cons2): minimize(cost)
#SYBL.ServiceUnitLevel
Str2: STRATEGY CASE ioCost < 3 Euro :
maximize( dataFreshness )
#SYBL.CodeRegionLevel
Cons4: CONSTRAINT dataAccuracy>90%
AND cost<4 Euro

9. Specifying uncertainties in
governance processes
§ Describe scopes in which
governance processes will be
applied
§ Rough set logics to compute
an objective approximation of
governance scopes for
dealing with missing data
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 9
G:GOVERNANCE_SCOPE
query:= location=buildingX &
type=JACE-545
CONSIDERING_UNCERTAINTY:
missing_data = "location<=’?’,type<=’*’"
AND
selection_strategy = optimistic AND
use_cache = false
S:STRATEGY CASE Fulfilled (CND1):
setUpdateRate(5s) FOR G
CONSIDERING_UNCERTAINTY:
Run_in_isolation = true AND
Keep_alive = 5min AND
Degree_parallelism = 200 AND
Tolerate_fault_percentage = 20% AND
Fallback_count = 2 AND
Time_to_next_fallback = 500ms
§ The elasticity control
strategies work in specific
governance scopes &
considering additional
uncertainty parameters

10. Resolving rough governance
scopes
§ Determine similar resources,
under attributes G with missing
information, by considering
problem-dependent uncertainty
parametrization
§ Based on the specified
selection_strategy
the U-
GovOps returns a governance
scope
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11. Dealing with actuations under
uncertainties
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 11

12. Experiments
§ Emulating an IoT Cloud System in the scenario
§ Infrastructures
§ Using Docker (~ 1000 docker containers) and
CentOS
§ https://hub.docker.com/r/dsgtuwien/govops-box/
§ U-GovOps: 4 Ubuntu VMs
§ Emulating
§ Missing or incomplete data
§ Actuation uncertainties
§ Using Dell Blockage tools to perform random fault
injection
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 12

13. Evaluation governance scopes
under missing data
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G1: GOVERNANCE_SCOPE query: location=building3&type=JACE-545||owner=TUW
CONSIDERING_UNCERTAINTY: missing_data =location<=’?’, owner<=’*’ AND
selection_strategy =optimistic;
M1: MONITORING abnormal_behavior := sensorAlert(G1)==true OR
heartBeatAVG(G1)>5min;
S1: STRATEGY CASE abnormal_behavior: setProtocol(’mqtt’),
changeUpdateRate(’5s’) FOR G1
CONSIDERING_UNCERTAINTY: running_inisolation =true AND keep_alive=1min AND
fallback_count =2 AND
tolerate_fault_percentage= 20% AND invocation_caching =true;
C1: CONSTRAINT cost<200 CONSIDERING_UNCERTAINTY: decision_confidence >=20%;
S2: STRATEGY CASE responseTime>250ms: scaleOut() CONSIDERING_UNCERTAINTY: …
F1 score for test
accuracy
• Controlled
experiments
• 50 reruns

14. Error rates for governance scopes
due to missing data
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The operator can make trade-offs by selecting appropriate strategies for
their specific purpose

15. Lost actuations rates for isolated
actuations
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Performance and additional cost must be paid for uncertainty management

16. Conclusions and Future Work
§ IoT cloud systems have complex types of
uncertainties that must be taken into account
§ Our uGovOps supports uncertainties in IoT cloud
management and engineering analytics
§ Language specification and enforcement
§ Runtime management foundations
§ Future work
§ Substantial improvement of uncertainty runtime
governance
§ Support new types of uncertainties
§ Incorporation of knowledge from uncertainty testing
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 16

17. Thanks for your
attention!
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/staff/truong
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 17