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

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4 Author(s)
Ioannis Kitsos ; Institute of Computer Science (ICS), Foundation for Research and Technology Hellas (FORTH), Heraklion GR-70013, Greece ; Antonis Papaioannou ; Nikos Tsikoudis ; Kostas Magoutis

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.

Published in:

2012 IEEE Network Operations and Management Symposium

Date of Conference:

16-20 April 2012