{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/85425"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/85425","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"VM and workload fingerprinting for software defined datacenters","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Ionescu, Dragos Ciprian"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Saman Amarasinghe."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-22T22:21:34Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/85425","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Saman Amarasinghe."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Ionescu, Dragos Ciprian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-06T15:41:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-06T15:41:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2013"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/85425"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 89-90)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["VM and workload fingerprinting for software defined datacenters"]}]}],"canonical_facts":{"dc:contributor.advisor":["Saman Amarasinghe."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Ionescu, Dragos Ciprian"],"dc:date.accessioned":["2014-03-06T15:41:07Z"],"dc:date.available":["2014-03-06T15:41:07Z"],"dc:date.issued":["2013"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 89-90)."],"dc:description.abstract":["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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/85425"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["VM and workload fingerprinting for software defined datacenters"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:34Z"}