Towards a two layers based scheduling schema for data aware strategies

The paper discusses various aspects of interest regarding the data management in the context of executing scientific workflows in the cloud. We offer a proper service-based solution e.g. Workflow as a Service, considering additional factors such as the control of data propagation and service level agreements. One important aspect is the optimization of resource allocation, and to accomplish this a dynamic allocation approach, based on certain heuristics, is adopted and tested. The aim of this paper is to introduce the basic concepts and some implementation details for a two layers based scheduling system that globally takes into account data access and space requirements. At the cloud level, we propose a scheduling strategy based on different Service Level Agreement (SLA) classes. The novelty of our strategy consists in using the SLA class of the user to provision a container that will execute the service, based on a dynamic computation of the number of resources. The user do not specify the exact amount of resources but a range. The key idea of the paper is in relaxing strict constraints on the number of resources for data management and on letting the 'System' to adjust the resources number according to the execution context. The first and second layers are validated through simulation and emulation conducted on the Grid'5000 testbed and for an heterogeneous context. The results of our experiments demonstrate the potential of our approaches, as general approaches for a better control of data movement and placement in cloud to execute scientific workflows. We also provide with insights to coupling the two proposed layers in a coherent way.