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Coriolis: Scalable VM Clustering in Clouds


Daniel Campello and Carlos Crespo, Florida International University; Akshat Verma, IBM Research-India; Raju Rangaswami, Florida International University; Praveen Jayachandran, IBM Research-India


The growing popularity of virtualized data centers and clouds has led to virtual machine sprawl, significantly increasing system management costs. We present Coriolis, a scalable system that analyzes virtual machine images and automatically clusters them based on content and/or semantic similarity. Image similarity analysis can improve in planning many management activities (e.g., migration, system administration, VM placement) and reduce their execution cost. However, clustering images based on similarity – content or semantic – requires large scale data processing and does not scale well. Coriolis uses (i) asymmetric similarity semantics and (ii) a hierarchical clustering approachwith a data access requirement that is linear in the number of images. This represents a significant improvement over conventional clustering approaches that incur quadratic complexity and therefore becoming prohibitively expensive in a cloud setting.

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@inproceedings {180154,
author = {Daniel Campello and Carlos Crespo and Akshat Verma and Raju Rangaswami and Praveen Jayachandran},
title = {Coriolis: Scalable {VM} Clustering in Clouds},
booktitle = {Proceedings of the 10th International Conference on Autonomic Computing ({ICAC} 13)},
year = {2013},
isbn = {978-1-931971-02-7},
address = {San Jose, CA},
pages = {101--105},
url = {},
publisher = {{USENIX}},


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