A Survey of Data-Intensive Scientific Workflow Management <s> Functional Architecture of SWfMSs <s> This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level without needing to worry about the particulars of the target execution systems. The paper describes general issues in mapping applications and the functionality of Pegasus. We present the results of improving application performance through workflow restructuring which clusters multiple tasks in a workflow into single entities. A real-life astronomy application is used as the basis for the study. <s> BIB001 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Functional Architecture of SWfMSs <s> We present Swift, a system that combines a novel scripting language called SwiftScript with a powerful runtime system based on CoG Karajan, Falkon, and Globus to allow for the concise specification, and reliable and efficient execution, of large loosely coupled computations. Swift adopts and adapts ideas first explored in the GriPhyN virtual data system, improving on that system in many regards. We describe the SwiftScript language and its use of XDTM to describe the logical structure of complex file system structures. We also present the Swift runtime system and its use of CoG Karajan, Falkon, and Globus services to dispatch and manage the execution of many tasks in parallel and grid environments. We describe application experiences and performance experiments that quantify the cost of Swift operations. <s> BIB002 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Functional Architecture of SWfMSs <s> Large-scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed and monitored by Scientific Workflow Management Systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in High Performance Computing (HPC) environments, such as clusters, grids and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor-intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed a workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables run-time queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches while the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support. <s> BIB003
The functional architecture of a SWfMS can be layered as follows  BIB001 BIB003 BIB002 : presentation, user services, WEP generation, WEP execution and infrastructure. Figure 3 shows this architecture. The higher layers take advantage of the lower layers to realize more concrete functionality. A user interacts with a SWfMS through presentation and realizes the desired functions at user services layer. A scientific workflow is processed at WEP generation layer to produce a WEP, which is executed at the WEP execution layer. The SWfMS accesses the physical resources through the infrastructure layer for scientific workflow execution. The combination of WEP generation layer, WEP execution layer and infrastructure layer corresponds to a scientific workflow execution engine.