Auto Scaling of Cloud Resources using Time Series and Machine Learning Prediction

Bhagavathiperumal, Sivasankari

Auto Scaling of Cloud Resources using Time Series and Machine Learning Prediction

Bhagavathiperumal, Sivasankari

Permalink

Publication Type:: Thesis
Issue Date:: 2020

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (332.5 kB)

Adobe PDF

Download thesisAdobe PDF (1.96 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Bhagavathiperumal, Sivasankari
dc.date.accessioned	2020-11-17T04:12:59Z
dc.date.available	2020-11-17T04:12:59Z
dc.date.issued	2020
dc.identifier.uri	http://hdl.handle.net/10453/144093
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Cloud platforms are becoming very popular as a means to host business applications, and most businesses are starting to use the cloud platform in order to reduce costs. A cloud platform is a shared resource that enables businesses to share the services Software as a service (SAAS), Infrastructure as a service (IAAS) or Anything as a service (XAAS), which are required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user’s requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Therefore, a process to predict the workload is required so that the cloud servers can handle the end user’s request and provide the required resources as Virtual Machines (VM) disruptively, so that the businesses using the cloud service only pay for the service they use, thus increasing the popularity of cloud computing. The workload consists of the application programs running on the machine and the users connected to and communicating with the computer’s applications. The computing resources are released based on the workload identified using the resource provisioning techniques, which are models used for enabling convenient on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications and services). In order to provide these resources dynamically without any interruption, an auto-scaling system is required to both manage the load and balance the service. The application should be capable of handling the load as it comes in and to ensure the cloud resources are not supplied in abundance when there are fewer requests coming to the server. This thesis introduces a framework to identify the current workload and predict the future load to the server. The results show the actual workload to the server and the level of requests expected in the future. But to fully appreciate the flexibility of identifying the future load in advance, it was important to identify a method that could in turn provide guidance with adjusting the resources supplied reducing the cost. Also, this would ensure the service level agreement (SLA) was met. The framework identified in this thesis carefully monitors the inputs to the server and analyses the load. The researches also applied deep learning techniques to predict the future load to the server, which produced a result with less root mean square error (RMSE) compared the other techniques. This thesis investigates five different methods to identify the future workload: Naïve, ARIMA, Linear regression, Exponential smoothing and Forward propagation. The logs of two datasets were analysed to predict the future resource for different time intervals along with computing the error between the predicted and actual value.	en_AU
dc.format	Thesis (MSc(CompSc))
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/144093/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Auto Scaling of Cloud Resources using Time Series and Machine Learning Prediction	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Cloud platforms are becoming very popular as a means to host business applications, and most businesses are starting to use the cloud platform in order to reduce costs. A cloud platform is a shared resource that enables businesses to share the services Software as a service (SAAS), Infrastructure as a service (IAAS) or Anything as a service (XAAS), which are required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user’s requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Therefore, a process to predict the workload is required so that the cloud servers can handle the end user’s request and provide the required resources as Virtual Machines (VM) disruptively, so that the businesses using the cloud service only pay for the service they use, thus increasing the popularity of cloud computing. The workload consists of the application programs running on the machine and the users connected to and communicating with the computer’s applications. The computing resources are released based on the workload identified using the resource provisioning techniques, which are models used for enabling convenient on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications and services). In order to provide these resources dynamically without any interruption, an auto-scaling system is required to both manage the load and balance the service. The application should be capable of handling the load as it comes in and to ensure the cloud resources are not supplied in abundance when there are fewer requests coming to the server. This thesis introduces a framework to identify the current workload and predict the future load to the server. The results show the actual workload to the server and the level of requests expected in the future. But to fully appreciate the flexibility of identifying the future load in advance, it was important to identify a method that could in turn provide guidance with adjusting the resources supplied reducing the cost. Also, this would ensure the service level agreement (SLA) was met. The framework identified in this thesis carefully monitors the inputs to the server and analyses the load. The researches also applied deep learning techniques to predict the future load to the server, which produced a result with less root mean square error (RMSE) compared the other techniques. This thesis investigates five different methods to identify the future workload: Naïve, ARIMA, Linear regression, Exponential smoothing and Forward propagation. The logs of two datasets were analysed to predict the future resource for different time intervals along with computing the error between the predicted and actual value.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/144093