Workload mix definition for benchmarking BPMN 2.0 Workflow Management Systems

Nowadays, enterprises broadly use Workflow Management Systems (WfMSs) to design, deploy, execute, monitor and analyse their automated business processes. Through the years, WfMSs evolved into platforms that deliver complex service oriented applications. In this regard, they need to satisfy enterprise-grade performance requirements, such as dependability and scalability. With the ever-growing number of WfMSs that are currently available in the market, companies are called to choose which product is optimal for their requirements and business models. Benchmarking is an established practice used to compare alternative products and leverages the continuous improvement of technology by setting a clear target in measuring and assessing performance. In particular, for service oriented WfMSs there is not yet a widely accepted standard benchmark available, even if workflow modelling languages such as Web Services Business Process Execution Language (WS-BPEL) and Business Process Model and Notation 2.0 (BPMN 2.0) have been adopted as the de-facto standards. A possible explanation on this deficiency can be given by the inherent architectural complexity of WfMSs and the very large number of parameters affecting their performance. However, the need for a standard benchmark for WfMSs is frequently affirmed by the literature. The goal of the BenchFlow approach is to propose a framework towards the first standard benchmark forassessing and comparing the performance of BPMN 2.0 WfMSs. To this end, the approach addresses a set of challenges spanning from logistic challenges, that are related to the collection of a representative set of usage scenarios,to technical challenges, that concern the specific characteristics of a WfMS. This work focuses on a subset of these challenges dealing with the definition of a representative set of process models and corresponding data that will be given as an input to the benchmark. This set of representative process models and corresponding data are referred to as the workload mix of the benchmark. More particularly, we first prepare the theoretical background for defining a representative workload mix. This is accomplished through identification of the basic components of a workload model for WfMS benchmarks, as well as the investigation of the impact of the BPMN 2.0 language constructs to the WfMS’s performance, by means of introducing the first BPMN 2.0 micro-benchmark. We proceed by collecting real-world process models for the identification of a representative workload mix. Therefore, the collection is analysed with respect to its statistical characteristics and also with a novel algorithm that detects and extracts the reoccurring structural patterns of the collection.The extracted reoccurring structures are then used for generating synthetic process models that reflect the essence of the original collection.The introduced methods are brought together in a tool chain that supports the workload mix generation. As a final step, we applied the proposed methods on a real-world case study, that bases on a collection of thousands of real-world process models and generates a representative workload mix to be used in a benchmark. The results show that the generated workload mix is successful in its application for stressing the WfMSs under test

Comparison of composition engines and identification of shortcomings with respect to cloud computing

Most workflow engines are currently not Cloud-aware. This is due to multiple reasons like no support for transparent scalability, no multi-tenancy support, no ability to store process related data in a Cloud storage, or no support for quality of service enforcements. Recently Cloud based workflow services appeared in the workflow landscape and promise to run workflows in the Cloud. This student reports evaluates current state of the art BPEL and BPMN workflow engines and Cloud based workflow services according to their Cloud- awareness and general workflow functionalities. Identified shortcomings are described and prioritized. As a result of this evaluation the workflow engine WSO2 Stratos is best suited for running workflows in the Cloud, but it lacks native clustering support and quality of service enforcement

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions

Intelligent monitoring of business processes using case-based reasoning

The work in this thesis presents an approach towards the effective monitoring of business processes using Case-Based Reasoning (CBR). The rationale behind this research was that business processes constitute a fundamental concept of the modern world and there is a constantly emerging need for their efficient control. They can be efficiently represented but not necessarily monitored and diagnosed effectively via an appropriate platform. Motivated by the above observation this research pursued to which extent there can be efficient monitoring, diagnosis and explanation of the workflows. Workflows and their effective representation in terms of CBR were investigated as well as how similarity measures among them could be established appropriately. The monitoring results and their following explanation to users were questioned as well as which should be an appropriate software architecture to allow monitoring of workflow executions. Throughout the progress of this research, several sets of experiments have been conducted using existing enterprise systems which are coordinated via a predefined workflow business process. Past data produced over several years have been used for the needs of the conducted experiments. Based on those the necessary knowledge repositories were built and used afterwards in order to evaluate the suggesting approach towards the effective monitoring and diagnosis of business processes. The produced results show to which extent a business process can be monitored and diagnosed effectively. The results also provide hints on possible changes that would maximize the accuracy of the actual monitoring, diagnosis and explanation. Moreover the presented approach can be generalised and expanded further to enterprise systems that have as common characteristics a possible workflow representation and the presence of uncertainty. Further work motivated by this thesis could investigate how the knowledge acquisition can be transferred over workflow systems and be of benefit to large-scale multidimensional enterprises. Additionally the temporal uncertainty could be investigated further, in an attempt to address it while reasoning. Finally the provenance of cases and their solutions could be explored further, identifying correlations with the process of reasoning