Multi-Phase Proactive Cloud Scheduling Framework Based on High Level Workflow and Resource Characterization

Workflows are used to represent applications in terms of the computational cost and the interdependencies of tasks. In parallel, clouds are a viable solution to execute complex applications in terms of performance and cost. This paper presents a cloud scheduling framework composed by multiple proactive phases that continuously compute and improve resource allocation and load distribution for workflow execution in cloud environments. The framework relies on a high-level characterization of resources and workflows to describe the capabilities provided by the infrastructure and the performance requirements to be met. Implementation and tests are based on the optimization of workflows executed on three scenarios: a private cloud, a hybrid cloud (private and public), and a multi-cloud setup. Results show improvement of run time performance compared to greedy approaches. Moreover, the framework is able to handle performance fluctuations, especially for long duration workflows.