A Survey of Data-Intensive Scientific Workflow Management <s> Askalon <s> Most existing Grid application development environments provide the application developer with a nontransparent Grid. Commonly, application developers are explicitly involved in tedious tasks such as selecting software components deployed on specific sites, mapping applications onto the Grid, or selecting appropriate computers for their applications. Moreover, many programming interfaces are either implementation-technology-specific (e.g., based on Web services [24]) or force the application developer to program at a low-level middleware abstraction (e.g., start task, transfer data [22, 153]). While a variety of graphical workflow composition tools are currently being proposed, none of them is based on standard modeling techniques such as Unified Modeling Language (UML). <s> BIB001 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Askalon <s> We present a new distributed performance analysis service of the ASKALON integrated Grid environment for computing runtime overheads of dynamic workflows in realtime based on event correlation techniques. We illustrate a formal method to express precise overhead correlation rules, including several performance contracts as quality of service parameters based on fuzzy logic to be enforced in dynamic environments though rescheduling, various runtime optimisations, and steering techniques. We demonstrate experimental results for two real applications from material chemistry and graphics rendering domains. <s> BIB002 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Askalon <s> From its start using supercomputers, scientific computing constantly evolved to the next levels such as cluster computing, meta-computing, or computational Grids. Today, Cloud Computing is emerging as the paradigm for the next generation of large-scale scientific computing, eliminating the need of hosting expensive computing hardware. Scientists still have their Grid environments in place and can benefit from extending them by leased Cloud resources whenever needed. This paradigm shift opens new problems that need to be analyzed, such as integration of this new resource class into existing environments, applications on the resources and security. The virtualization overheads for deployment and starting of a virtual machine image are new factors which will need to be considered when choosing scheduling mechanisms. In this paper we investigate the usability of compute Clouds to extend a Grid workflow middleware and show on a real implementation that this can speed up executions of scientific workflows. <s> BIB003 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Askalon <s> We present GroudSim, a Grid and Cloud simulation toolkit for scientific applications based on a scalable simulation-independent discrete-event core. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to calculation of costs, and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based approach. <s> BIB004 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Askalon <s> Public commercial clouds emerged as new and attractive resource provisioning option for scientific computing. This new alternative raises new challenges for users of such clouds, since optimizing the completion time of scientific applications might substantially increase the monetary cost of leasing cloud resources. In this paper, we first propose a set of basic rescheduling operations covering a broad set of scenarios for reducing the costs of running scientific workflows in clouds. Based on them, we design two heuristic scheduling algorithms. The first algorithm aims at reducing the cost of resource provisioning while still attaining the optimal make span. The second algorithm further reduces the costs to meet a budget constraint with a small increase in the make span. The experiments conducted using real-world and synthetic workflow applications demonstrate important benefits compared to related state-of-the-art approaches. <s> BIB005
Askalon BIB001 is also a SWfMS initially designed for a grid environment. In the presentation layer, it provides a GUI, through which a scientific workflow can be modeled using Unified Modeling Language. It also exploits an XML-based language to model workflows. In the user services layer, it provides on-line workflow execution monitoring functionality through workflow execution monitoring and dynamic workflow steering to deal with exceptions in dynamic and unpredictable execution environments BIB002 . In the WEP generation layer, Askalon optimizes the workflow representation with loops, i.e. within DCG structures, to a DAG workflow structure. In the WEP execution layer, Askalon exploits an execution engine to provide workflow fault-tolerance at the levels of workflow, activity and control-flow. It can exploit static and hybrid scheduling, e.g. rescheduling because of unpredictable changes in the execution environment. In the infrastructure layer, Askalon uses a resource manager to discover and reserve available resources and to deploy executable tasks in the grid environment. Askalon is able to execute scientific workflow in cloud environment by dynamic creation of VMs with available cloud images BIB003 . In the cloud environment, Askalon can estimate the cost of scientific workflow execution by simulation BIB004 and provide dynamic resource provisioning and task scheduling under budget constraint BIB005 . Moreover, Askalon can execute scientific workflows in a federated multisite cloud BIB003 , i.e. a multisite cloud composed of resources from different providers. Nevertheless, it schedules tasks in computing nodes without considering the organization of computing resources, i.e. which VMs are at the same site, for optimization. This method just takes the VMs as the grid computing nodes without considering the features of multisite resources, e.g. the difference of data transfer rate, resource sharing for intra-site and inter-site, etc.