An Autonomic Management System for Choreography-based Workflows on Grids and Clouds

Future computing environments will integrate cloud platforms with existing computational infrastructures providing frameworks to combine traditional Grid services with on-demand Cloud services, so that additional resources can be requested during peak loads and released after that. Nowadays, workflows are the preferred means for the composition of services into added value service chains representing functional business processes or complex scientific experiments. This paper describes Sunflower an innovative P2P agent-based framework for configuring, enacting, managing and adapting workflows on hybrid computing infrastructures. To orchestrate Grid and Cloud services, Sunflower uses a bio-inspired autonomic choreography model and integrates the scheduling algorithm with a provisioning component that can dynamically launch virtual machines in a Cloud infrastructure to provide on-demand services in peak-load situations.