A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Ontological Formalization for Workflow-based Computational Experiments

AbstractWorkflow-based computational experiment is a widespread way to organize distributed simulations. But the lack of IT experience and skills is the critical issue which scientists usually face with. By this paper we describe the reasoning capabilities, which are obtained from the proposed hierarchical structure for expert's knowledge formalization. The contribution of this paper is the ontological representation of a structure, which make end-users to deal with domain models compiled of fine-grained domain and infrastructural entities in order to generate an executable workflow as a result. A task of forecasting of storm surges and decision support for gates maneuvering is presented a use-case of the paper

Supporting Quality of Service in Scientific Workflows

While workflow management systems have been utilized in enterprises to support businesses for almost two decades, the use of workflows in scientific environments was fairly uncommon until recently. Nowadays, scientists use workflow systems to conduct scientific experiments, simulations, and distributed computations. However, most scientific workflow management systems have not been built using existing workflow technology; rather they have been designed and developed from scratch. Due to the lack of generality of early scientific workflow systems, many domain-specific workflow systems have been developed. Generally speaking, those domain-specific approaches lack common acceptance and tool support and offer lower robustness compared to business workflow systems. In this thesis, the use of the industry standard BPEL, a workflow language for modeling business processes, is proposed for the modeling and the execution of scientific workflows. Due to the widespread use of BPEL in enterprises, a number of stable and mature software products exist. The language is expressive (Turingcomplete) and not restricted to specific applications. BPEL is well suited for the modeling of scientific workflows, but existing implementations of the standard lack important features that are necessary for the execution of scientific workflows. This work presents components that extend an existing implementation of the BPEL standard and eliminate the identified weaknesses. The components thus provide the technical basis for use of BPEL in academia. The particular focus is on so-called non-functional (Quality of Service) requirements. These requirements include scalability, reliability (fault tolerance), data security, and cost (of executing a workflow). From a technical perspective, the workflow system must be able to interface with the middleware systems that are commonly used by the scientific workflow community to allow access to heterogeneous, distributed resources (especially Grid and Cloud resources). The major components cover exactly these requirements: Cloud Resource Provisioner Scalability of the workflow system is achieved by automatically adding additional (Cloud) resources to the workflow system’s resource pool when the workflow system is heavily loaded. Fault Tolerance Module High reliability is achieved via continuous monitoring of workflow execution and corrective interventions, such as re-execution of a failed workflow step or replacement of the faulty resource. Cost Aware Data Flow Aware Scheduler The majority of scientific workflow systems only take the performance and utilization of resources for the execution of workflow steps into account when making scheduling decisions. The presented workflow system goes beyond that. By defining preference values for the weighting of costs and the anticipated workflow execution time, workflow users may influence the resource selection process. The developed multiobjective scheduling algorithm respects the defined weighting and makes both efficient and advantageous decisions using a heuristic approach. Security Extensions Because it supports various encryption, signature and authentication mechanisms (e.g., Grid Security Infrastructure), the workflow system guarantees data security in the transfer of workflow data. Furthermore, this work identifies the need to equip workflow developers with workflow modeling tools that can be used intuitively. This dissertation presents two modeling tools that support users with different needs. The first tool, DAVO (domain-adaptable, Visual BPEL Orchestrator), operates at a low level of abstraction and allows users with knowledge of BPEL to use the full extent of the language. DAVO is a software that offers extensibility and customizability for different application domains. These features are used in the implementation of the second tool, SimpleBPEL Composer. SimpleBPEL is aimed at users with little or no background in computer science and allows for quick and intuitive development of BPEL workflows based on predefined components

Enhancing Workflow with a Semantic Description of Scientific Intent

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HyDRA Hybrid workflow Design Recommender Architecture

Workflows are a way to describe a series of computations on raw e-Science data. These data may be MRI brain scans, data from a high energy physics detector or metric data from an earth observation project. In order to derive meaningful knowledge from the data, it must be processed and analysed. Workflows have emerged as the principle mechanism for describing and enacting complex e-Science analyses on distributed infrastructures such as grids. Scientific users face a number of challenges when designing workflows. These challenges include selecting appropriate components for their tasks, spec- ifying dependencies between them and selecting appropriate parameter values. These tasks become especially challenging as workflows become increasingly large. For example, the CIVET workflow consists of up to 108 components. Building the workflow by hand and specifying all the links can become quite cumbersome for scientific users.Traditionally, recommender systems have been employed to assist users in such time-consuming and tedious tasks. One of the techniques used by recommender systems has been to predict what the user is attempting to do using a variety of techniques. These techniques include using workflow se- mantics on the one hand and historical usage patterns on the other. Semantics-based systems attempt to infer a user’s intentions based on the available semantics. Pattern-based systems attempt to extract usage patterns from previously-constructed workflows and match those patterns to the workflow un- der construction. The use of historical patterns adds dynamism to the suggestions as the system can learn and adapt with “experience”. However, in cases where there are no previous patterns to draw upon, pattern-based systems fail to perform. Semantics-based systems, on the other hand infer from static information, so they always have something to draw upon. However, that information first has to be encoded into the semantic repository for the system to draw upon it, which is a time-consuming and tedious task in it self. Moreover, semantics-based systems do not learn and adapt with experience. Both approaches have distinct, but complementary features and drawbacks. By combining the two approaches, the drawbacks of each approach can be addressed.This thesis presents HyDRA, a novel hybrid framework that combines frequent usage patterns and workflow semantics to generate suggestions. The functions performed by the framework include; a) extracting frequent functional usage patterns; b) identifying the semantics of unknown components; and c) generating accurate and meaningful suggestions. Challenges to mining frequent patterns in- clude ensuring that meaningful and useful patterns are extracted. For this purpose only patterns that occur above a minimum frequency threshold are mined. Moreover, instead of just groups of specific components, the pattern mining algorithm takes into account workflow component semantics. This allows the system to identify different types of components that perform a single composite function. One of the challenges in maintaining a semantic repository is to keep the repository up-to-date. This involves identifying new items and inferring their semantics. In this regard, a minor contribution of this research is a semantic inference engine that is responsible for function b). This engine also uses pre-defined workflow component semantics to infer new semantic properties and generate more accurate suggestions. The overall suggestion generation algorithm is also presented.HyDRA has been evaluated using workflows from the Laboratory of Neuro Imaging (LONI) repos- itory. These workflows have been chosen for their structural and functional characteristics that help� to evaluate the framework in different scenarios. The system is also compared with another existing pattern-based system to show a clear improvement in the accuracy of the suggestions generated

A MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONS

In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented

Distribution pattern-driven development of service architectures

Distributed systems are being constructed by composing a number of discrete components. This practice is particularly prevalent within the Web service domain in the form of service process orchestration and choreography. Often, enterprise systems are built from many existing discrete applications such as legacy applications exposed using Web service interfaces. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed in a distributed environment. However, the amount of code required to realise these distribution patterns is considerable. In this paper, we propose a distribution pattern-driven approach to service composition and architecting. We develop, based on a catalog of patterns, a UML-compliant framework, which takes existing Web service interfaces as its input and generates executable Web service compositions based on a distribution pattern chosen by the software architect