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Governing Elastic IoT Cloud Systems under Uncertainties

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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.

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Governing Elastic IoT Cloud Systems under Uncertainties

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 11. Dealing with actuations under uncertainties CloudCom 2015, 1 Dec 2015, Vancouver, Canada 11
  12. 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. 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. 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. 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. 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. 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

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