Why This Matters

Diverse scientific domains have specialized workflow requirements making it difficult to develop single universal workflow system, yet completely custom development for each domain is expensive and error-prone. This work innovates by providing meta-tools and domain-specific languages that enable rapid development of customized workflow systems. The model-based approach enables both constraint specification and automated configuration generation ensuring correct system deployment.

What We Did

This paper presents techniques for designing scientific workflow management systems using model-based approaches and meta-tools that enable custom workflow system development. The work develops ComponentML domain-specific language for specifying component-based systems and uses model-driven architecture to enable composition of component libraries. The approach supports specification of constraints on components and automatic generation of deployment configurations that satisfy those constraints.

Key Results

The framework successfully supports development of customized workflow systems for scientific domains through specification of domain-specific models and constraints. Results show effective automatic generation of deployment configurations that satisfy complex constraints including performance and composition restrictions. The approach demonstrates practical feasibility of model-driven development for scientific workflow systems.

Full Abstract

Cite This Paper

@inproceedings{Saxena2010,
  author = {Saxena, Tripti and Dubey, Abhishek and Balasubramanian, Daniel and Karsai, Gabor},
  booktitle = {2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems},
  title = {Enabling self-management by using model-based design space exploration},
  year = {2010},
  organization = {IEEE},
  pages = {137--144},
  abstract = {Reconfiguration and self-management are important properties for systems that operate in hazardous and uncontrolled environments, such as inter-planetary space. These systems need a reconfiguration mechanism that provides recovery from individual component failures as well as the ability to dynamically adapt to evolving mission goals. One way to provide this functionality is to define a model of alternative system configurations and allow the system to choose the current configuration based on its current state, including environmental parameters and goals. The primary difficulties with this approach are (1) the state space of configurations can grow very large, which can make explicit enumeration infeasible, and (2) the component failures and evolving system goals must be somehow encoded in the system configuration model. This paper describes an online reconfiguration method based on model-based designspace exploration. We symbolically encode the set of valid system configurations and assert the current system state and goals as symbolic constraints. Our initial work indicates that this method scales and is capable of providing effective online dynamic reconfiguration.},
  category = {conference},
  contribution = {lead},
  file = {:Saxena2010-Enabling_Self-Management_by_Using_Model-based_Design_Space_Exploration.pdf:PDF},
  keywords = {scientific workflows, model-driven development, domain-specific languages, workflow systems, constraint specification}
}
Quick Info
Year 2010
Keywords
scientific workflows model-driven development domain-specific languages workflow systems constraint specification
Research Areas
middleware scalable AI
Search Tags

Enabling, self, management, model, design, space, exploration, scientific workflows, model-driven development, domain-specific languages, workflow systems, constraint specification, middleware, scalable AI, 2010, Saxena, Dubey, Balasubramanian, Karsai