Towards Reference Passing in Web Service and Workflow-Based Applications

Wieland, Matthias; Görlach, Katharina; Schumm, David; Leymann, Frank

doi:10.1109/edoc.2009.17

Cited by 32 publications

(8 citation statements)

References 9 publications

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“…Wieland et al [24] provide a concept of pointers in serviceoriented architecture to pass data by reference rather than by value from Web services. This can reduce the data load on the centralized engine and reduce the network traffic.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Orchestration of Data-centric Workflows Using the Object Modeling System

Javadi

Tomko

Sinnott

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Data-centric and service-oriented workflows are commonly used in scientific research to enable the composition and execution of complex analysis on distributed resources. Although there are a plethora of orchestration frameworks to implement workflows, most of them are not suitable to execute data-centric workflows. The main issue is transferring output of service invocations through a centralized orchestration engine to the next service in the workflow, which can be a bottleneck for the performance of a data-centric workflow. In this paper, we propose a flexible and lightweight workflow framework based on the Object Modeling Systems (OMS). Moreover, we take advantage of the OMS architecture to deploy and execute data-centric workflows in a decentralized manner to avoid passing through the centralized engine. The proposed framework is implemented in context of the Australian Urban Research Infrastructure Network (AURIN) project which is an initiative aiming to develop an e-Infrastructure supporting research in the urban and built environment research disciplines. Performance evaluation results using spatial data-centric workflows show that we can reduce 20% of the workflows execution time while using Cloud resources in the same network domain.

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Section: Related Workmentioning

confidence: 99%

Decentralized Orchestration of Data-centric Workflows Using the Object Modeling System

Javadi

Tomko

Sinnott

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…Data is moved by reference during workflow execution in a reference passing approach (Wieland et al 2009). Here the producer service passes the reference of the data to the consumer service via the orchestrator.…”

Section: In the Perspective Of Data Transfer Optimisationmentioning

confidence: 99%

Reducing data transfer in big-data workflows: the computation-flow delegated approach

Nunes¹,

Deshpande

2019

J. of Data, Inf. and Manag.

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Existing orchestrated bioinformatics workflow execution approaches necessitate the transfer of datasets from biological data services to the analysis tool (computation) services of the workflow for various data analysis. This model of moving data to computation during workflow execution weakens the performance of the workflow especially when the orchestrated bioinformatics workflow has to handle big-data in it. Since the size of the analysis tools are much smaller than the datasets size in a workflow, in this paper, to minimize the dataflow and improve workflow performance, we propose a novel computation-flow delegated (CFD) approach. The CFD approach lets the tool services of the workflow to dynamically migrate analysis tools towards the datasets to perform computation on data side during workflow execution. We use a set of mobile agents to operate the CFD approach and present a mobile agent-based computation-flow delegation framework (MABCFD) to execute the workflow tasks. We implement the prototype of the MABCFD framework and analyze the performance of the CFD approach empirically by executing in isolation workflow patterns (sequence, fan-out and fan-in) common to bioinformatics applications. Performance analysis shows that the computation-driven CFD approach consistently outperforms the existing data-driven approaches across all patterns and scales favorably with data size.

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“…The model suitable for execution is called Executable Workow Networks (EWFN), a Petri nets dialect. The concept of pointers in service-oriented architectures (Wieland et al, 2009) allows Web services to pass data by reference rather than by value. This has the advantage that the workow engine is disburdened of handling all data passing between the orchestrated Web Services, which helps to reduce network trafc and processing time.…”

Section: Techniques In Data Transfer Optimisationmentioning

confidence: 99%

Decentralised Orchestration of Service-Oriented Scientific Workflows

Barker

Buyya

2011

Proceedings of the 1st International Conference on Cloud Computing and Services Science

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Service-oriented workflows in the scientific domain are commonly composed as Directed Acyclic Graphs (DAGs), formed from a collection of vertices and directed edges. When orchestrating service-oriented DAGs, intermediate data are typically routed through a single centralised engine, which results in unnecessary data transfer, increasing the execution time of a workflow and causing the engine to become a performance bottleneck. This paper introduces an architecture for deploying and executing a service-oriented DAG-based workflows across a peer-to-peer proxy network. A workflow is divided into a set of vertices, disseminated to a group of proxies and executed without centralised control over a peer-to-peer proxy network. Through a Web services implementation, we demonstrate across PlanetLab that by reducing intermediate data transfer and by sharing the workload between distributed proxies, end-to-end workflows are sped up. Furthermore, our architecture is non-intrusive: Web services owned and maintained by different institutions do not have to be altered prior to execution.

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Towards Reference Passing in Web Service and Workflow-Based Applications

Abstract: In a Service-Oriented Architecture (SOA)

Cited by 32 publications

References 9 publications

Decentralized Orchestration of Data-centric Workflows Using the Object Modeling System

Decentralized Orchestration of Data-centric Workflows Using the Object Modeling System

Reducing data transfer in big-data workflows: the computation-flow delegated approach

Decentralised Orchestration of Service-Oriented Scientific Workflows

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