Building Blocks of Mayan: Componentizing the eScience Workflows Through Software-Defined Service...
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Transcript of Building Blocks of Mayan: Componentizing the eScience Workflows Through Software-Defined Service...
Building Blocks of Mayan:Componentizing the eScience Workflows Through
Software-Defined Service Composition
Pradeeban Kathiravelu*, Tihana Galinac Grbac+, Luís Veiga*
*INESC-ID Lisboa & Instituto Superior Técnico, Universidade de Lisboa, Portugal+University of Rijeka, Croatia
23rd IEEE International Conference on Web Services (ICWS 2016)June 27 - July 2, 2016, San Francisco, USA.
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Overview
1 Introduction
2 Mayan Approach
3 Evaluation
4 Conclusion
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Introduction
Introduction
eScience workflowsComputation-intensive.Execute on highly distributed networks.
Complex service compositions aggregating web servicesTo automate scientific and enterprise business processes.
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Introduction
Motivation
Increasing demand forData quality and Quality of Service (QoS).Better Performance (Shorter completion times and higher throughput).Geo-distribution (workflows and compositions).
Need for additional control and flexibility.Exploring Trade-off: Efficiency vs. Accuracy.Leveraging Software-Defined Approaches (from SDN).
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Introduction
Goals
Scalable Distributed Executions.High Scalability.Better orchestration.Data Quality Assurance.
Multi-Tenanted Environments.Isolation Guarantees.Differentiated Quality of Service (QoS).
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Introduction
Contributions
Support for,Adaptive execution of scientific workflows.Flexible service composition.Reliable large-scale service composition.Efficient selection of service instances.
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Mayan Approach
Mayan
Extensible SDN approach for cloud-scale service composition
Driven by:Loose couplingMessage-oriented Middleware (MOM)Availability of a logically centralized control plane
Leveraging OpenDaylight SDN controller as the core.Modular, as OSGi bundles.Additional advanced features.
State of executions and transactions stored in the controller distributeddata tree.Clustered and federated deployments.
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Mayan Approach
Services as the building blocks of Mayan
Prototypical Example:
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Mayan Approach
Software-Defined Service Composition
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Mayan Approach
Multiple Implementations and Deployments of a Service
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Mayan Approach
Software-Defined Service Composition
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Mayan Approach
Services as the building blocks of Mayan
Prototypical Example:
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Mayan Approach
Too many requests on the fly?
Prototypical Example:
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Mayan Approach
Alternative Deployment/Implementation
Prototypical Example:
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Mayan Approach
Mayan Services Registry: Modelling Language
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Mayan Approach
Service Composition Representation
<Service3,(<Service1, Input1>, <Service2, Input2>)>
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Mayan Approach
Alternative Implementations and Deployments
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Mayan Approach
Mayan Higher Level Deployment Architecture:Multi-Domain Workflows
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Mayan Approach
Connecting Services View with the Network View
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Mayan Approach
Connecting Services View with the Network View
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Evaluation
Evaluation System Configurations
Evaluation Approach:Smaller physical deployments in a cluster.Larger deployments as simulations and emulations (Mininet).
Evaluated Deployment:Service Composition Implementations.
Web services frameworks.Apache Hadoop MapReduce.Hazelcast In-Memory Data Grid.
OpenDaylight SDN Controller.
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Evaluation
Preliminary Assessments
A workflow performing distributed data cleaning andconsolidation [PK 2015].
A distributed web service composition.vs.Mayan approach with the extended SDN architecture.
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Evaluation
Speedup and Horizontal Scalability
No negative scalability in larger distributions.100% more positive scalability for larger deployments.
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Evaluation
Memory consumption in the Service Nodes
Initial coordination overhead in memory for smaller deployments.Minimal overhead for larger deployments.
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Conclusion
Related Work
MapReduce for efficient service compositions [SD 2014].
But we should not forget the registry!
Palantir: SDN for MapReduce performance with the network proximitydata [ZY 2014].A multi-domain deployment of SDN for communitynetworks [PK 2016].
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Conclusion
Conclusion
SDN-based approach that enables large scale flexibility withperformance
Components in eScience workflows as building blocks of a distributedplatform.Service composition with web services and distributed executionframeworks.Multi-tenanted multi-domain executions.
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Conclusion
Conclusion
SDN-based approach that enables large scale flexibility withperformance
Components in eScience workflows as building blocks of a distributedplatform.Service composition with web services and distributed executionframeworks.Multi-tenanted multi-domain executions.
Future WorkMayan should further be deployed and evaluated on physicalgeo-distributed nodes.Extending Software-defined service composition for the networkfunctions in service composition of middlebox actions.
Load balancing.Firewalls.
Adapting as an NFV framework for service function chaining.
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Conclusion
References
PK 2015 Kathiravelu, Pradeeban, Helena Galhardas, and Luís Veiga. "∂u∂u Multi-Tenanted Framework: DistributedNear Duplicate Detection for Big Data." On the Move to Meaningful Internet Systems: OTM 2015Conferences. Springer International Publishing, 2015.
SD 2014 Deng, Shuiguang, et al. "Top-Automatic Service Composition: A Parallel Method for Large-Scale ServiceSets." Automation Science and Engineering, IEEE Transactions on 11.3 (2014): 891-905.
ZY 2014 Yu, Ze, et al. "Palantir: Reseizing network proximity in large-scale distributed computing frameworks usingsdn." 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014.
PK 2016 Kathiravelu, Pradeeban, and Luıs Veiga. "CHIEF: Controller Farm for Clouds of Software-DefinedCommunity Networks." Software Defined Systems (SDS), 2016 IEEE International Symposium on. IEEE,2016.
Thank you!Questions?
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