Towards the Scientific Cloud Workflow Architecture

Scientific workflows emerged as a technology that enables scientists to undertake computational scientific experiments. Workflow enactors map workflow tasks onto distributed resources, hiding the inherent complexity of distributed infrastructures to the users. In the past, while the emphasis has been focused in adapting the workflow structure onto the resources, today the emergence of the cloud computing paradigm enables us to adapt the resources to the workflow tasks based on their characteristics. In this paper, we examine the concept of Cloud Scientific Workflow, and propose a new architectural approach based on autonomic principles, guided by a combination of high-level and low-level policies. High-level policies enable the workflow enactor to choose among a number of workflow structure transformations that better suit the underlying resources dynamically depending on the context, whereas low-level policies enable the autonomic resource manager to adjust the required computational power to the workload derived from a scientific workflow specification, exploiting the cloud elasticity property, and to cope with performance fluctuations or unexpected events in the cloud infrastructure. The novelty of our approach is the combination of both policies that can lead to higher degrees of dynamism. The key enabling architectural components for such a dynamism are the petri-net based performance models for implementing high-level policies and MOSt-CB system for the adaptation to multi-cloud environments.