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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
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
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/
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
IoT Cloud
Infrastructure
Uncertainty
Taxonomy
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 5
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
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
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
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
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
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
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 10
Dealing with actuations under
uncertainties
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 11
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
Evaluation governance scopes
under missing data
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 13
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
Error rates for governance scopes
due to missing data
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 14
The operator can make trade-offs by selecting appropriate strategies for
their specific purpose
Lost actuations rates for isolated
actuations
CloudCom 2015, 1 Dec 2015, Vancouver, Canada 15
Performance and additional cost must be paid for uncertainty management
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
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

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Governing Elastic IoT Cloud Systems under Uncertainties

  • 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 CloudCom 2015, 1 Dec 2015, Vancouver, Canada 5 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 CloudCom 2015, 1 Dec 2015, Vancouver, Canada 10
  • 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 CloudCom 2015, 1 Dec 2015, Vancouver, Canada 13 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 CloudCom 2015, 1 Dec 2015, Vancouver, Canada 14 The operator can make trade-offs by selecting appropriate strategies for their specific purpose
  • 15. Lost actuations rates for isolated actuations CloudCom 2015, 1 Dec 2015, Vancouver, Canada 15 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