Recommending Resources to Cloud Applications based on Custom Templates Composition

Emerging interdisciplinary data-intensive applications in science and engineering fields (e.g. bioinformatics, cybermanufacturing) demand the use of high-performance computing resources. However, data-intensive applications' local resources usually present limited capacity and availability due to sizable upfront costs. The applications requirements warrant intelligent resource 'abstractions' coupled with 'reusable' approaches to save time and effort in deploying cyberinfrastructure (CI). In this paper, we present a novel 'custom templates' management middleware to overcome this scarcity of resources by use of advanced CI management technologies/protocols to on-demand deploy data-intensive applications across distributed/federated cloud resources. Our middleware comprises of a novel resource recommendation scheme that abstracts user requirements of data-intensive applications and matches them with federated cloud resources using custom templates in a catalog. We evaluate the accuracy of our recommendation scheme in two experiment scenarios. The experiments involve simulating a series of user interactions with diverse applications requirements, also feature a real-world data-intensive application case study. Our experiment results show that our scheme improves the resource recommendation accuracy by up to 21%, compared to the existing schemes.