Abstract

Analytics are in the core of many emerging applications and can greatly benefit from the abundance of data and the progress in the processing capabilities of modern hardware. Still, new challenges arise with the extreme complexity of deciding how to execute analytics workflows given the plethora of choices of various cloud providers, the fragmented nature of diverse Big Data technologies, and the difficult task of resource provisioning to dynamically satisfy the demands of running streaming analytics over time. In this paper, we present SheerMP, a prototype system that optimizes streaming analytics workflows of multiple users, across Big Data platforms and networks of compute clusters/clouds. SheerMP provides end-to-end support for Analytics-as-a-Service providers offering a holistic framework serving diverse optimization and adaptive resource allocation scenarios over a variety of streaming Big Data platforms.