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

Abstract

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.

DOI: 10.1109/NOMS.2012.6211879

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Cite this paper

@article{Kitsos2012AdaptingDW, title={Adapting data-intensive workloads to generic allocation policies in cloud infrastructures}, author={Ioannis Kitsos and Antonis Papaioannou and Nikos Tsikoudis and Kostas Magoutis}, journal={2012 IEEE Network Operations and Management Symposium}, year={2012}, pages={25-33} }