This chapter introduces a scheduling approach for cross-domain scientific workflow execution with timerelated QoS evaluation. Generally, scientific workflow execution often spans self-managing administrative domains to achieving global collaboration advantage. In practice, it is infeasible for a domain-specific application to disclose its process details for privacy or security reasons. Consequently, it is a challenging endeavor to coordinate scientific workflows and its distributed domain-specific applications from service invocation perspective. Therefore, in this chapter, the authors aim at proposing a collaborative scheduling approach, with time-related QoS evaluation, for navigating cross-domain collaboration. Under this collaborative scheduling approach, a private workflow fragment could maintain temporal consistency with a global scientific workflow in resource sharing and task enactments. Furthermore, an evaluation is presented to demonstrate the scheduling approach.
Top1 Introduction
In the past few years, some computing infrastructures, e.g., grid infrastructure, have been emerged for accommodating powerful computing and for enhancing resource sharing capabilities required by cross-organizational workflow application (Wieczorek, 2005; Fox, 2006). It is a new special type of workflow that often underlies many large-scale complex e-science applications such as climate modeling, structural biology and chemistry, medical surgery or disaster recovery simulation (Ludäscher, 2005; Bowers, 2008; Zhao, 2006). This new type of scientific workflow applications is gaining more and more momentums due to their key role in e-Science and cyber-infrastructure applications. As scientific workflows are typically data-centric and dataflow-oriented “analysis pipelines” (Ludäscher, 2005; McPhillips, 2005), scientists often need to “glue” together various cross-domain services such as cross-organizational data management, analysis, simulation, and visualization services (Yan, 2007; Rygg, 2008). Compared with business workflows, scientific workflows have special features such as computation, data or transaction intensity, less human interactions, and a larger number of activities (Wieczorek, 2005). Accordingly, scientific workflow applications frequently require collaborative patterns marked by multiple domain-specific applications from different organizations. An engaged domain-specific application often contributes a definite local computing goal to global scientific workflow execution. Typically, in this loose coupled application environment, goal-specific scientists are rather individualistic and more likely to create their own “knowledge discovery workflow” by taking advantage of available services (Ludäscher, 2005). It promotes scientific collaboration in form of service invocation for achieving certain computing goals.
To facilitate scientific workflow’s development and execution, cross-domain workflow modeling and scheduling are key topics that currently cause more and more attentions (Wieczorek, 2005; Yan, 2007;Yu, J., & Buyya, R., 2005;Yu, J., Buyya, R., & Tham, C. K., 2005). For example, Yu and Buyya (Yu, J., & Buyya, R., 2005) provided a general taxonomy of scientific workflow, in which workflow design, workflow scheduling, fault tolerance, and data movement are four key features associated with the development and execution of a scientific workflow management system in Grid environment. Furthermore, they believed that a scientific workflow paradigm could greatly enhance scientific collaboration through spanning multiple administrative domains to obtain specific processing capabilities. Here, scientific collaborations are often navigated by data-dependency and temporal-dependency relations among goal-specific domain applications, in which a domain-specific application is often implemented as a local workflow fragment deployed inside a self-managing organization for providing the demanded services in time. In a grid computing infrastructure, a service for scientific collaboration is often called a grid service that address resource discovery, security, resource allocation, and other concerns (Foster, 2001). For cross-organizational collaboration, existing (global) analysis techniques often mandate every domain-specific service to unveil all individual behaviors for scientific collaboration (Chiu, 2004). Unfortunately, such an analysis is infeasible when a domain-specific service refuses to disclose its process details for privacy or security reasons (Dumitrescu, 2005; Liu, 2006). Therefore, it is always a challenging endeavor to coordinate a scientific workflow and its distributed domain-specific applications (local workflow fragments for producing domain-specific service), especially when a local workflow fragment is engaged in different scientific workflow executions in a concurrent environment. Generally, a local workflow fragment for producing domain-specific service is often deployed inside a self-governing organization, which could be treated as a private workflow fragment of the self-governing organization.