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Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data./
Author:	Kellogg, Tad.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	133 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548454
ISBN:	9798516961519

Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data.
Kellogg, Tad.

Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 133 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (D.C.S.)--Colorado Technical University, 2021.

This item must not be sold to any third party vendors.

As modern cloud provider growth continues, the need to optimize physical resources to contain and reduce energy consumption is a predominant problem. Prior research has shown solutions for the virtual machine placement problem (VMPP) that can optimize physical resources and reduce energy consumption. However, as the VMPP is an NP-hard type problem, research continues for more optimized approximations. The research presented in this dissertation has demonstrated a unique approximation as a VMPP solution. The use of a convolutional neural network (CNN) and K-Means combinational algorithm-based time-series clustering artifact to identify the initial placement with predicted future workload physical host cohesion as a VMPP solution. The research has demonstrated a rejection of the null hypothesis, in which the energy consumption for the artifact VMPP solution, as measured in the number of activated physical servers, was shown to be less than the energy consumption of a random distribution placement algorithm as a VMPP solution (p < 0.001). The reduction was shown in seven simulation scenarios in which the initial activation of hosts was sustained during a simulated 24 hours. The reduction average of 7.5 physical hosts also matched the performance of prior research VMPP solution algorithms. Finally, the use of CNN and K-Means algorithms combined for time-series clustering can now be considered a valid methodology for VMPP solution research.

ISBN: 9798516961519Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Convolutional neural network

Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Schmitt, Alexa.
502 $a Thesis (D.C.S.)--Colorado Technical University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a As modern cloud provider growth continues, the need to optimize physical resources to contain and reduce energy consumption is a predominant problem. Prior research has shown solutions for the virtual machine placement problem (VMPP) that can optimize physical resources and reduce energy consumption. However, as the VMPP is an NP-hard type problem, research continues for more optimized approximations. The research presented in this dissertation has demonstrated a unique approximation as a VMPP solution. The use of a convolutional neural network (CNN) and K-Means combinational algorithm-based time-series clustering artifact to identify the initial placement with predicted future workload physical host cohesion as a VMPP solution. The research has demonstrated a rejection of the null hypothesis, in which the energy consumption for the artifact VMPP solution, as measured in the number of activated physical servers, was shown to be less than the energy consumption of a random distribution placement algorithm as a VMPP solution (p < 0.001). The reduction was shown in seven simulation scenarios in which the initial activation of hosts was sustained during a simulated 24 hours. The reduction average of 7.5 physical hosts also matched the performance of prior research VMPP solution algorithms. Finally, the use of CNN and K-Means algorithms combined for time-series clustering can now be considered a valid methodology for VMPP solution research.
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