University of Oulu

Predictive model creation approach using layered subsystems quantified data collection from LTE L2 software system

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Author: Puerto Valencia, Jose1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Information Processing Science, Information Processing Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 18.8 MB)
Pages: 98
Persistent link: http://urn.fi/URN:NBN:fi:oulu-201907192705
Language: English
Published: Oulu : J. Puerto Valencia, 2019
Publish Date: 2019-07-19
Thesis type: Master's thesis
Tutor: Mäntylä, Mika
Liggesmeyer, Peter
Reviewer: Siirtola, Antti
Mäntylä, Mika
Liggesmeyer, Peter
Description:

Abstract

The road-map to a continuous and efficient complex software system’s improvement process has multiple stages and many interrelated on-going transformations, these being direct responses to its always evolving environment. The system’s scalability on this on-going transformations depends, to a great extent, on the prediction of resources consumption, and systematic emergent properties, thus implying, as the systems grow bigger in size and complexity, its predictability decreases in accuracy. A predictive model is used to address the inherent complexity growth and be able to increase the predictability of a complex system’s performance. The model creation processes are driven by the recollection of quantified data from different layers of the Long-term Evolution (LTE) Data-layer (L2) software system. The creation of such a model is possible due to the multiple system analysis tools Nokia has already implemented, allowing a multiple-layers data gathering flow. The process starts by first, stating the system layers differences, second, the use of a layered benchmark approach for the data collection at different levels, third, the design of a process flow organizing the data transformations from recollection, filtering, pre-processing and visualization, and forth, As a proof of concept, different Performance Measurements (PM) predictive models, trained by the collected pre-processed data, are compared. The thesis contains, in parallel to the model creation processes, the exploration, and comparison of various data visualization techniques that addresses the non-trivial graphical representation of the in-between subsystem’s data relations. Finally, the current results of the model process creation process are presented and discussed. The models were able to explain 54% and 67% of the variance in the two test configurations used in the instantiation of the model creation process proposed in this thesis.

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Copyright information: © Jose Puerto Valencia, 2019. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.