Biblio
With the development of cloud computing, cloud workflow systems are widely accepted by more and more enterprises and individuals (namely tenants). There exists mass tenant workflow instances running in cloud workflow systems. How to implement the three-level (i.e., data, performance, execution ) isolation and privacy protection among these tenant workflow instances is challenging. To address this issue, this paper presents a novel cloud workflow model supporting multi-tenants with privacy protection. With the presented model, a framework of cloud workflow engine based on the extended jBPM4 is proposed by adopting layered management thought, virtualization technology and sandbox mechanism. By extending the jBPM4 (java Business Process Management) engine, the prototype system of the proposed cloud workflow engine is implemented and applied in the ceramic cloud service platform (denoted as CCSP). The application effect demonstrates that our proposal can be used to implement the three-level isolation and privacy protection between mass various tenant workflow instances in cloud workflow systems.
Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.
The paradigm shift from traditional BPM to Subject-oriented BPM (S-BPM) is accounted to identifying independently acting subjects. As such, they can perform arbitrary actions on arbitrary objects. Abstract State Machines (ASMs) work on a similar basis. Exploring their capabilities with respect to representing and executing S-BPM models strengthens the theoretical foundations of S-BPM, and thus, validity of S-BPM tools. Moreover it enables coherent intertwining of business process modeling with executing of S-BPM representations. In this contribution we introduce the framework and roadmap tackling the exploration of the ASM approach in the context of S-BPM. We also report the major result, namely the implementation of an executable workflow engine with an Abstract State Machine interpreter based on an existing abstract interpreter model for S-BPM (applying the ASM refinement concept). This workflow engine serves as a baseline and reference implementation for further language and processing developments, such as simulation tools, as it has been developed within the Open-S-BPM initiative.