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vmBBProfiler: A BlackBox Profiling Approach to Quantify Sensitivity of Virtual Machines to Shared Cloud Resources
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Computer Networking, DISCO)ORCID iD: 0000-0001-9194-010X
University of Sydney, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Computer Networking, DISCO)ORCID iD: 0000-0002-9446-8143
2017 (English)In: Computing, ISSN 0010-485X, E-ISSN 1436-5057Article in journal (Refereed) Published
Abstract [en]

Virtualized Data Centers are packed with numerous web and cloud servicesnowadays. In such large infrastructures, providing reliable service platforms dependsheavily on efficient sharing of physical machines (PMs) by virtual machines (VMs).To achieve efficient consolidation, performance degradation of co-located VMs mustbe correctly understood, modeled, and predicted. This work is a major step towardunderstanding such baffling phenomena by not only identifying, but also quantifyingsensitivity of general purpose VMs to their demanded resources. vmBBProfiler, ourproposed system in this work, is able to systematically profile behavior of any generalpurpose VM and calculate its sensitivity to system provided resources such as CPU,Memory, and Disk. vmBBProfiler is evaluated using 12 well-known benchmarks,varying from pure CPU/Mem/Disk VMs to mixtures of them, on three different PMsin our VMware-vSphere based private cloud. Extensive empirical results conductedover 1200h of profiling prove the efficiency of our proposed models and solutions; italso opens doors for further research in this area. vmBBProfiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources (PDF Download Available). Available from: https://www.researchgate.net/publication/315649278_vmBBProfiler_a_black-box_profiling_approach_to_quantify_sensitivity_of_virtual_machines_to_shared_cloud_resources [accessed Oct 4, 2017].

Place, publisher, year, edition, pages
Springer, 2017.
Keyword [en]
Performance degradation, Virtualization, Cloud computing
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-64566DOI: 10.1007/s00607-017-0552-yOAI: oai:DiVA.org:kau-64566DiVA: diva2:1155760
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Available from: 2017-11-09 Created: 2017-11-09 Last updated: 2017-11-09

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