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Computer Science
Distributed Systems

Flexibility for Distributed Workflows

Profile image of Thomas BauerThomas Bauer

Concepts, Methodologies, Tools, and Applications

https://doi.org/10.4018/978-1-4666-4153-2.CH072
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Abstract

This chapter shows how flexibility can be realized for distributed workflows. The capability to dynamically adapt workflow instances during runtime (e.g., to add, delete or move activities) constitutes a fundamental challenge for any workflow management system (WfMS). While there has been significant research on ad-hoc workflow changes and on related correctness issues, there exists only little work on how to provide respective runtime flexibility in an enterprise-wide context as well. Here, scalability at the presence of high loads constitutes an essential requirement, often necessitating distributed (i.e., piecewise) control of a workflow instance by different workflow servers, which should be as independent from each other as possible. This chapter presents advanced concepts and techniques for enabling ad-hoc workflow changes in a distributed WfMS as well. Our focus is on minimizing the communication costs among workflow servers, while ensuring a correct execution behavior as wel...

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Selim Kalayci

2013

Scientific applications keep getting more complex, resulting in the need for more computational resources than may be available to scientists locally. As a result, for many scientists utilization of remote and heterogeneous computational resources has become a standard practice. However, effective utilization of these resources, especially for large-scale workflow applications, necessitates the employment of software tools that are efficient and adaptive. In this study, we propose a generic framework for the decentralization and run-time adaptation for the execution of large-scale workflow applications that span across diverse and heterogeneous resource domains. By exploiting the recurring DAG patterns, we come up with corresponding decentralization and adaptation patterns to be employed by peer workflow orchestration tools to cope with various resource-based challenges in the execution environment. Our framework adopts separation of concerns and consequently does not alter the busi...

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A Framework for Optimizing Distributed Workflow Executions
Bharat Kumar

1999

A central problem in workflow concerns optimizing the distribution of work in a workflow: how should the execution of tasks and the management of tasks be distributed across multiple processing nodes (i.e., computers). In some cases task management or execution may be at a processing node with limited functionality, and so it is useful to optimize translations of (sub-)workflow schemas into flowcharts, that can be executed in a restricted environment, e.g., in a scripting language or using a flowchart-based workflow engine. This paper presents a framework for optimizing the physical distribution of workflow schemas, and the mapping of sub-workflow schemas into flowcharts. We provide a general model for representing essentially any distribution of a workflow schema, and for representing a broad variety of execution strategies. The model is based on families of “communicating flowcharts” (CFs). In the framework, a workflow schema is first rewritten as a family of CFs that are essentially atomic and execute in parallel. The CFs can be grouped into “clusters”. Several CFs can be combined to form a single CF, which is useful when executing a sub-schema on a limited processor. Local rewriting rules are used to specify equivalencepreserving transformations. We developed a set of formulas to quantify the metrics used for choosing a near optimal set of CF clusters for executing a workflow. The current paper focuses primarily on ECA-based workflow models, such as Flowmark, Meteor and Mentor, and condition-action based workflow models, such as ThinkSheet and Vortex.

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Related topics

  • Computer Science
  • Distributed Computing
  • Flexibility in engineering design
  • Workflow Technology
  • Workflow Management System
  • Workflow Engine
  • Enterprise Resource Planning
  • Communication Cost
  • Dynamic Adaptation
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