VM and workload fingerprinting for software defined datacenters

In this work we develop strategies for mining telemetry streams in virtualized clusters to automatically discover relationships between sets of virtual machines. Examples of relationships include correlations between virtual machines, similarities in resource consumption patterns or dominant resources, and similarities in metric variations. The main challenge in our approach is to transform the raw captured data consisting of resource usage and VM-related metrics into a meaningful fingerprint that identifies the virtual machine and describes its performance. In our analysis we try to determine which of these metrics are relevant and how they can be expressed as a light-weight and robust fingerprint that offers insight about the status of the machine.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Ionescu, Dragos Ciprian

Advisor dc:contributor.advisor

• Saman Amarasinghe.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/85425

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85425