A framework for early design and prototyping of service-oriented applications with design patterns

Service-oriented computing is playing an important role in several domains. Today the biggest shift in mainstream design and programming is toward service-oriented applications. However, the service paradigm raises a bundle of problems that did not exist in traditional component-based development where abstraction, encapsulation, and modularity were the only main concerns. Due to their distributed, dynamic, and heterogeneous nature, service-oriented software applications require us to discover, document, and share new design patterns at the service- and architecture-level. Moreover, service-oriented applications are hard to design and validate, and demand for new foundational theories, modeling notations and analysis techniques.In line to such a vision, this article presents a framework, called SCA-PatternBox, to design and prototype service-oriented applications with design patterns. The framework relies on the OASIS standard Service Component Architecture (SCA) and on SCA component implementation types, such as SCA-Java, for supporting an “implementation-oriented” approach to service-oriented architecture modeling and to the definition and instantiation of design patterns. Moreover, in order to provide formally verified design patterns, SCA-PatternBox allows the formal specification and analysis of the functional behavioral aspects of a design pattern using a formal service specification language called SCA-ASM (Service Component Architecture-Abstract State Machine). As major evaluation of the framework, two case studies and lessons learned are presented. A final comparison of existing design pattern languages is also reported.     

Managing heterogeneous multi-system tasks to support enterprise-wide operations

The computing environment in most medium-sized and large enterprises involves old main-frame based (legacy) applications and systems as well as new workstation-based distributed computing systems. The objective of the METEOR project is to support multi-system workflow applications that automate enterprise operations. This paper deals with the modeling and specification of workflows in such applications. Tasks in our heterogeneous environment can be submitted through different types of interfaces on different processing entities. We first present a computational model for workflows that captures the behavior of both transactional and non-transactional tasks of different types. We then develop two languages for specifying a workflow at different levels of abstraction: the Workflow Specification Language (WFSL) is a declarative rule-based language used to express the application-level interactions between multiple tasks, while the Task Specification Language (TSL) focuses on the issues related to individual tasks. These languages are designed to address the important issues of inter-task dependencies, data formatting, data exchange, error handling, and recovery. The paper also presents an architecture for the workflow management system that supports the model and the languages. Recommended by: Omran Bukhres and e. Kühn     

Common motifs in scientific workflows: An empirical analysis

Workflow technology continues to play an important role as a means for specifying and enacting computational experiments in modern science. Reusing and re-purposing workflows allow scientists to do new experiments faster, since the workflows capture useful expertise from others. As workflow libraries grow, scientists face the challenge of finding workflows appropriate for their task, understanding what each workflow does, and reusing relevant portions of a given workflow. We believe that workflows would be easier to understand and reuse if high-level views (abstractions) of their activities were available in workflow libraries. As a first step towards obtaining these abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna, Wings, Galaxy and Vistrails. Our analysis has resulted in a set of scientific workflow motifs that outline (i) the kinds of data-intensive activities that are observed in workflows (Data-Operation motifs), and (ii) the different manners in which activities are implemented within workflows (Workflow-Oriented motifs). These motifs are helpful to identify the functionality of the steps in a given workflow, to develop best practices for workflow design, and to develop approaches for automated generation of workflow abstractions.     

A new optimization phase for scientific workflow management systems

Scientific workflows have emerged as an important tool for combining the computational power with data analysis for all scientific domains in e-science, especially in the life sciences. They help scientists to design and execute complex in silico experiments. However, with rising complexity it becomes increasingly impractical to optimize scientific workflows by trial and error. To address this issue, we propose to insert a new optimization phase into the common scientific workflow life cycle. This paper describes the design and implementation of an automated optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers a versatile programming interface (API), which enables easy integration of arbitrary optimization methods. We have used this API to develop an example plugin for parameter optimization that is based on a Genetic Algorithm. Two use cases taken from the areas of structural bioinformatics and proteomics demonstrate how our framework facilitates setup, execution, and monitoring of workflow parameter optimization in high performance computing e-science environments.     

Patterns-based evaluation of open source BPM systems: The cases of jBPM,OpenWFE, and Enhydra Shark

In keeping with the proliferation of free software development initiatives and the increased interest in the business process management domain, many open source workflow and business process management systems have appeared during the last few years and are now under active development. This upsurge gives rise to two important questions: What are the capabilities of these systems? and How do they compare to each other and to their closed source counterparts? In other words: What is the state-of-the-art in the area?. To gain an insight into these questions, we have conducted an in-depth analysis of three of the major open source workflow management systems – jBPM, OpenWFE, and Enhydra Shark, the results of which are reported here. This analysis is based on the workflow patterns framework and provides a continuation of the series of evaluations performed using the same framework on closed source systems, business process modelling languages, and web-service composition standards. The results from evaluations of the three open source systems are compared with each other and also with the results from evaluations of three representative closed source systems: Staffware, WebSphere MQ, and Oracle BPEL PM. The overall conclusion is that open source systems are targeted more toward developers rather than business analysts. They generally provide less support for the patterns than closed source systems, particularly with respect to the resource perspective, i.e. the various ways in which work is distributed amongst business users and managed through to completion.     

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.     

Soundness of workflow nets: classification, decidability, and analysis

Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets. We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.     

Providing Architectural Languages and Tools Interoperability through Model Transformation Technologies

Many architectural languages have been proposed in the last 15 years, each one with the chief aim of becoming the ideal language for specifying software architectures. What is evident nowadays, instead, is that architectural languages are defined by stakeholder concerns. Capturing all such concerns within a single, narrowly focused notation is impossible. At the same time, it is also impractical to define and use a “universal” notation, such as UML. As a result, many domain-specific notations for architectural modeling have been proposed, each one focusing on a specific application domain, analysis type, or modeling environment. As a drawback, a proliferation of languages exists, each one with its own specific notation, tools, and domain specificity. No effective interoperability is possible to date. Therefore, if a software architect has to model a concern not supported by his own language/tool, he has to manually transform (and, eventually, keep aligned) the available architectural specification into the required language/tool. This paper presents DUALLy, an automated framework that allows architectural languages and tools interoperability. Given a number of architectural languages and tools, they can all interoperate thanks to automated model transformation techniques. DUALLy is implemented as an Eclipse plugin. Putting it in practice, we apply the DUALLy approach to the Darwin/FSP ADL and to a UML2.0 profile for software architectures. By making use of an industrial complex system, we transform a UML software architecture specification in Darwin/FSP, make some verifications by using LTSA, and reflect changes required by the verifications back to the UML specification.     

Scientific workflows for computational reproducibility in the life sciences: Status,challenges and opportunities

With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance.The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.     

Automated verification of design patterns: A case study

Representing design decisions for complex software systems, tracing them to code, and enforcing them throughout the lifecycle are pressing concerns for software architects and developers. To be of practical use, specification and modeling languages for software design need to combine rigor with abstraction and simplicity, and be supported by automated design verification tools that require minimal human intervention. This paper examines closely the use of the visual language of Codecharts for representing design decisions and demonstrate the process of verifying the conformance of a program to the chart. We explicate the abstract semantics of segments of the Java package java.awt as a finite structures, specify the Composite design pattern as a Codechart and unpack it as a set of formulas, and prove that the structure representing the program satisfies the formulas. We also describe a set of tools for modeling design patterns with Codecharts and for verifying the conformance of native (plain) Java programs to the charts.     

作业流访问控制策略

作业流是一系列任务，它由一个任务集和一个任务关系集组成，其中，任务关系集说明各任务的执行顺序。该文在给出作业流定义的基础上，提出了一种基于作业流Petri网描述的访问控制机制，并介绍了该机制的实现。     

Visualization workflows for level-12 HUC scales: Towards an expert system for watershed analysis in a distributed computing environment

Visualization workflows are important services for expert users to analyze watersheds when using our HydroTerre end-to-end workflows. Analysis is an interactive and iterative process and we demonstrate that the expert user can focus on model results, not data preparation, by using a web application to rapidly create, tune, and calibrate hydrological models anywhere in the continental USA (CONUS). The HydroTerre system captures user interaction for provenance and reproducibility to share modeling strategies with modelers. Our end-to-end workflow consists of four workflows. The first is data workflows using Essential Terrestrial Variables (ETV) data sets that we demonstrated to construct watershed models anywhere in the CONUS (Leonard and Duffy, 2013). The second is data-model workflows that transform the data workflow results to model inputs. The model inputs are consumed in the third workflow, model workflows (Leonard and Duffy, 2014a) that handle distribution of data and model within High Performance Computing (HPC) environments. This article focuses on our fourth workflow, visualization workflows, which consume the first three workflows to form an end-to-end system to create and share hydrological model results efficiently for analysis and peer review. We show how visualization workflows are incorporated into the HydroTerre infrastructure design and demonstrate the efficiency and robustness for an expert modeler to produce, analyze, and share new hydrological models using CONUS national datasets.     

Combining unit and specification-based testing for meta-model validation and verification

Meta-models play a cornerstone role in Model-Driven Engineering as they are used to define the abstract syntax of modelling languages, and so models and all sorts of model transformations depend on them. However, there are scarce tools and methods supporting their Validation and Verification (V&V), which are essential activities for the proper engineering of meta-models.In order to fill this gap, we propose two complementary meta-model V&V languages. The first one has similar philosophy to the xUnit framework, as it enables the definition of meta-model unit test suites comprising model fragments and assertions on their (in-)correctness. The second one is directed to express and verify expected properties of a meta-model, including domain and design properties, quality criteria and platform-specific requirements.As a proof of concept, we have developed tooling for both languages in the Eclipse platform, and illustrate its use within an example-driven approach for meta-model construction. The expressiveness of our languages is demonstrated by their application to build a library of meta-model quality issues, which has been evaluated over the ATL zoo of meta-models and some OMG specifications. The results show that integrated support for meta-model V&V (as the one we propose here) is urgently needed in meta-modelling environments.     

工作流技术在新一代电信运营综合管理支撑系统中的应用研究

介绍了工作流技术并以电信运营综合管理系统 (Business &Operation Support System,简称 BOSS)为背景 ,分析了工作流技术的具体实现 ,主要包括工作流技术介绍 ,工作流支撑的 BOSS架构、BOSS参考模型、BOSS体系结构、BOSS数据中心模型等几个方面     

Fundamentals of control flow in workflows

Abstract. Although workflow management emerged as a research area well over a decade ago, little consensus has been reached as to what should be essential ingredients of a workflow specification language. As a result, the market is flooded with workflow management systems, based on different paradigms and using a large variety of concepts. The goal of this paper is to establish a formal foundation for control-flow aspects of workflow specification languages, that assists in understanding fundamental properties of such languages, in particular their expressive power. Workflow languages can be fully characterized in terms of the evaluation strategy they use, the concepts they support, and the syntactic restrictions they impose. A number of results pertaining to this classification will be proven. This should not only aid those developing workflow specifications in practice, but also those developing new workflow engines. Received 16 January 2001 / 13 November 2002 This research is supported by an ARC SPIRT grant “Component System Architecture for an Open Distributed Enterprise Management System with Configurable Workflow Support” between QUT and Mincom.     

Science in the Cloud: Allocation and Execution of Data-Intensive Scientific Workflows

An important challenge for the adoption of cloud computing in the scientific community remains the efficient allocation and execution of data-intensive scientific workflows to reduce execution time and the size of transferred data. The transferred data overhead is becoming significant with emerging scientific workflows that have input/output files and intermediate data products ranging in the hundreds of gigabytes. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters, and has been proved to be NP-complete. This paper proposes an evolutionary approach for task allocation on public clouds considering data transfer and execution time. In our framework, a solution is represented using an allocation chromosome that encodes the allocation of tasks to nodes, and an ordering chromosome that defines the execution order according to the scientific workflow representation. We propose a multi-objective optimization that relies on a cloud cost model and employs tailored evolution operators. Starting from a population of possible solutions, we employ crossover and mutation operators on both chromosomes aiming at optimizing the data transferred between nodes as well as the total workflow runtime. The crossover operators combine parts of solutions to reduce data overhead, whereas the mutation operators swamp between parts of the same chromosome according to pre-defined rules. Our experimental study compares between the proposed approach and current state-of-the art approaches using synthetic and real-life workflows. Our algorithm performs similarly to existing heuristics for small workflows and shows up to 80 % improvements for larger synthetic workflows. To further validate our approach we compare between the allocation and scheduling obtained by our approach with that obtained by popular scientific workflow managers, when real workflows with hundreds of tasks are executed on a public cloud. The results show a 10 % improvement in runtime over existing schedulers, caused by a 80 % reduction in transferred data and optimized allocation and ordering of tasks. This improved data locality has greater impact as it can be employed to improve and study data provenance and facilitate data persistence for scientific workflows.