Trust enforcement through self-adapting cloud workflow orchestration

Abstract Providing runtime intelligence of a workflow in a highly dynamic cloud execution environment is a challenging task due the continuously changing cloud resources. Guaranteeing a certain level of workflow Quality of Service (QoS) during the execution will require continuous monitoring to detect any performance violation due to resource shortage or even cloud service interruption. Most of orchestration schemes are either configuration, or deployment dependent and they do not cope with dynamically changing environment resources. In this paper, we propose a workflow orchestration, monitoring, and adaptation model that relies on trust evaluation to detect QoS performance degradation and perform an automatic reconfiguration to guarantee QoS of the workflow. The monitoring and adaptation schemes are able to detect and repair different types of real time errors and trigger different adaptation actions including workflow reconfiguration, migration, and resource scaling. We formalize the cloud resource orchestration using state machine that efficiently captures different dynamic properties of the cloud execution environment. In addition, we use validation model checker to validate our model in terms of reachability, liveness, and safety properties. Extensive experimentation is performed using a health monitoring workflow we have developed to handle dataset from Intelligent Monitoring in Intensive Care III (MIMICIII) and deployed over Docker swarm cluster. A set of scenarios were carefully chosen to evaluate workflow monitoring and the different adaptation schemes we have implemented. The results prove that our automated workflow orchestration model is self-adapting, self-configuring, react efficiently to changes and adapt accordingly while supporting high level of Workflow QoS.

[1]  Mauro Iacono,et al.  Exploiting mean field analysis to model performances of big data architectures , 2014, Future Gener. Comput. Syst..

[2]  Inderveer Chana,et al.  QoS-Aware Autonomic Resource Management in Cloud Computing , 2015, ACM Comput. Surv..

[3]  Manish Parashar,et al.  CometCloud: An Autonomic Cloud Engine , 2011, CloudCom 2011.

[4]  David Bernstein,et al.  Intercloud Security Considerations , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[5]  Munindar P. Singh,et al.  An evidential model of distributed reputation management , 2002, AAMAS '02.

[6]  Raul Castro Fernandez,et al.  Integrating scale out and fault tolerance in stream processing using operator state management , 2013, SIGMOD '13.

[7]  Mohamed Adel Serhani,et al.  Towards an Efficient Federated Cloud Service Selection to Support Workflow Big Data Requirements , 2018 .

[8]  Boualem Benatallah,et al.  Web Service Composition , 2015 .

[9]  Kishor S. Trivedi,et al.  Analytical Modeling of Reactive Autonomic Management Techniques in IaaS Clouds , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[10]  Alessio Lomuscio,et al.  MCMAS: A Model Checker for the Verification of Multi-Agent Systems , 2009, CAV.

[11]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[12]  Ellis Solaiman,et al.  Orchestrating BigData Analysis Workflows , 2017, IEEE Cloud Computing.

[13]  Aziz Nasridinov,et al.  A QoS-Aware Performance Prediction for Self-Healing Web Service Composition , 2012, 2012 Second International Conference on Cloud and Green Computing.

[14]  Wolfgang Barth,et al.  Nagios: System and Network Monitoring , 2006 .

[15]  Rajiv Ranjan,et al.  A Taxonomy and Survey of Cloud Resource Orchestration Techniques , 2017, ACM Comput. Surv..

[16]  Boualem Benatallah,et al.  A Multi-Dimensional Trust Model for Processing Big Data Over Competing Clouds , 2018, IEEE Access.

[17]  Abraham Silberschatz,et al.  HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads , 2009, Proc. VLDB Endow..

[18]  José A. B. Fortes,et al.  Sky Computing , 2009, IEEE Internet Computing.

[19]  Rajkumar Buyya,et al.  Interconnected Cloud Computing Environments , 2014, ACM Comput. Surv..

[20]  Wei Hu,et al.  Trust constrained workflow scheduling in cloud computing , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[21]  Rajiv Ranjan,et al.  An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art , 2013, Computing.

[22]  Aman Kansal,et al.  Q-clouds: managing performance interference effects for QoS-aware clouds , 2010, EuroSys '10.

[23]  Jemal H. Abawajy,et al.  Determining Service Trustworthiness in Intercloud Computing Environments , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[24]  Carlos Becker Westphall,et al.  Toward an architecture for monitoring private clouds , 2011, IEEE Communications Magazine.

[25]  Peter Zadrozny,et al.  Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources , 2013 .

[26]  Huan Liu,et al.  CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment , 2015, IEEE Transactions on Services Computing.

[27]  Adriyendi.  Multi-Attribute Decision Making Using Simple Additive Weighting and Weighted Product in Food Choice , 2015 .

[28]  Benny Rochwerger,et al.  Monitoring Service Clouds in the Future Internet , 2010, Future Internet Assembly.

[29]  Jun Huang,et al.  QoS-Aware Service Composition for Converged Network-Cloud Service Provisioning , 2014, 2014 IEEE International Conference on Services Computing.

[30]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[31]  Christof Fetzer,et al.  A Novel Approach to QoS Monitoring in the Cloud , 2011, 2011 First International Conference on Data Compression, Communications and Processing.

[32]  Athman Bouguettaya,et al.  Reputation Propagation in Composite Services , 2009, 2009 IEEE International Conference on Web Services.

[33]  Fabio Panzieri,et al.  QoS–Aware Clouds , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[34]  Kai Li,et al.  An Orchestration Based Cloud Auto-Healing Service Framework , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[35]  Mohd Fadzil Hassan,et al.  Renegotiation in Service Level Agreement Management for a Cloud-Based System , 2015, ACM Comput. Surv..

[36]  Srikumar Venugopal,et al.  Elastic Business Process Management: State of the art and open challenges for BPM in the cloud , 2014, Future Gener. Comput. Syst..