Adapting data-intensive workloads to generic allocation policies in cloud infrastructures

Resource allocation policies in public Clouds are today largely agnostic to requirements that distributed applications have from their underlying infrastructure. As a result, assumptions about data-center topology that are built-into distributed data-intensive applications are often violated, impacting performance and availability goals. In this paper we describe a management system that discovers a limited amount of information about Cloud allocation decisions - in particular VMs of the same user that are collocated on a physical machine - so that data-intensive applications can adapt to those decisions and achieve their goals. Our distributed discovery process is based on either application-level techniques (measurements) or a novel lightweight and privacy-preserving Cloud management API proposed in this paper. Using the distributed Hadoop file system as a case study we show that VM collocation in a Cloud setup occurs in commercial platforms and that our methodologies can handle its impact in an effective, practical, and scalable manner.