On-demand Provisioning of Infrastructure, Middleware and Services for Simulation Workflows

Abstract

Service orientation is a mainstream paradigm in business applications and gains even greater acceptance in the very active field of eScience. In SOC service binding strategies have been defined to specify the point in time a service can be discovered and selected for use, namely static binding, dynamic binding at deployment or at run time, and dynamic service deployment. The basic assumption in all these strategies is that the software stack and infrastructure necessary to execute the services are already available. While in service-based business applications this is typically a valid assumption in scientific applications it is often not the case. Therefore, in this work we introduce a new binding strategy for services we call on-demand provisioning which entails provisioning of the software stack necessary for the service and subsequent dynamic deployment of the service itself. Towards this goal, we also contribute a middleware architecture that enables the provisioning of the software stack -functionality unavailable in conventional service middlewares. We demonstrate the approach and the capabilities of the middleware and the current state of the implementation of our approach. For this purpose we use an example application from the field of eScience that comprises a scientific workflow management system for simulations.

Service Selection for On-Demand Provisioned Services

“…Although we already achieved some promising results regarding some single aspects of our approach [1] [21], an evaluation of an overall end to end scenario is still missing.…”

“…In our previous work we have developed the architecture of a system supporting our approach for on-demand provisioning and de-provisioning of workflow execution middleware and services needed for the execution of (simulation) workflows [1]. We present the architecture in Fig.…”

“…Not provisioned services have to be explicitly provisioned before they can be used. In our previous work we worked out an extended classification for service binding strategies [1]. Typical strategies for static and dynamic service binding rely on provisioned services.…”

“…We developed an approach and architecture for the on-demand provisioning and de-provisioning of workflow execution middleware and services for simulation workflows [1]. In this approach, services including their underlying middleware and infrastructure are provisioned not until they are needed, and de-provisioned when they are not needed anymore.…”

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

“…Vukojevic et al [24] present a very similar solution to ours in a service oriented scenario to support simulation workflows. Their aim is to provide and redeem services on-demand according to the progress of the workflow.…”