[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124498-en":3,"doc-seo-124498-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124498,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning–Based Traffic Prediction for Peering Selection in ISP Networks - Thesis","The thesis commissioned by Elisa Oyj investigates how to improve utilization of private peering versus public peering and transit providers to reduce networking costs. It develops a machine learning program that leverages legacy traffic data and historical patterns to predict future traffic volumes. The study examines Internet structure, traffic exchange among parties, and related cost drivers, answering whether legacy data can forecast future trends and which factors are required for accurate predictions. Multiple ML models are evaluated to support proactive connectivity planning and better investment decisions.","Machine Learning–Based Traffic Prediction for Peering Selection in ISP Networks  \nMikko Laakso  \n2025 Laurea  \nLaurea University of Applied Sciences  \nMachine Learning–Based Traffic Prediction for Peering Selection in ISP Networks  \nMikko Laakso  \nBIT, Cyber Security  \nThesis  \nDecember 2025  \nLaurea University of Applied Sciences Abstract  \nDegree Programme in Business Information Technology, Cyber Security Bachelor  \nMikko Laakso  \nMachine learning in peering selection  \nYear 2025 Number of pages 22  \nThe work was commissioned by Elisa Oyj, a Finland-based telecommunications company. The thesis aims to improve Elisa’s utilization of private peering vs. public peering or transit providers to reduce networking costs.  \nThe goal for this thesis was to develop a program which uses machine learning to examine past traffic patterns in order to predict future traffic amounts to enable better utilization of investment and proactive building of connectivity.  \nThe research method used was a methods approach that combined quantitative analysis, qualitive insights and applied research.  \nTo understand the topic, the thesis researches the structure of the Internet and how traffic is passed between different parties and the costs associated with these methods. The following research questions are answered: Can we use legacy data to predict future trends? What factors do we need to consider to make good predictions?  \nThe defined research questions were answered in the literature review.  \nThe thesis reaches the set goal of creating a program which utilizes different machine learning models to predict future traffic amounts between Elisa and possible peering partners.  \nKeywords: Private Peering, BGP, Transit, Machine learning  \nContents  \n1 Introduction ............................................................................................ 6  \n2 Background ............................................................................................. 6  \n2.1 Public Peering, Private Peering and Transit .............................................. 6  \n2.2 Point Of Presence ............................................................................ 8  \n2.3 Public Peering ................................................................................ 8  \n2.4 Overview of networking concepts relevant to peering (e.g. , BGP, latency, bandwidth) ............................................................................................. 9  \n2.4.1 Border Gateway Protocol (BGP) .................................................... 9  \n2.4.2 Latency................................................................................. 9  \n2.4.3 Bandwidth and Throughput ......................................................... 9  \n2.5 Traditional methods and criteria used by ISPs ......................................... 10  \n2.5.1 Traffic Analysis ..................................................................... 10  \n2.5.2 Geographical Proximity, Physical Access and Policies ........................ 10  \n2.5.3 Business Relationships and Reciprocity .......................................... 10  \n2.6 Limitations of Current Approaches ...................................................... 11  \n3 Research metohodology ............................................................................ 11  \n3.1 Research approach and data collection................................................. 11  \n4 Machine Learning Fundamentals .................................................................. 12  \n4.1 Overview of Machine Learning ........................................................... 12  \n4.2 ML and its categories ...................................................................... 12  \n4.2.1 Supervised, unsupervised and semi-supervised Learning ..................... 12  \n4.2.2 Reinforcement Learning ........................................................... 12  \n4.3 Specific techniques applicable to the problem (e.g. , decision trees, neura","cbCaigwAlyRRXGTP","https://ap.wps.com/l/cbCaigwAlyRRXGTP","pdf",546723,1,22,"English","en",105,"# Introduction\n## Background\n## Point Of Presence\n## Public Peering\n## Overview of networking concepts relevant to peering (e.g. BGP, latency, bandwidth)\n## Traditional methods and criteria used by ISPs\n## Limitations of Current Approaches\n# Research methodology\n## Research approach and data collection\n# Machine Learning Fundamentals\n## Overview of Machine Learning\n## ML and its categories\n## Specific techniques applicable to the problem\n## Data Collection and Preprocessing\n# Feature Engineering\n## Identifying Key Features\n## Features relevant to peering decisions\n## Feature Importance Assessment\n# Model Development\n## Choosing the Right Model\n## Selecting ML models based on data characteristics\n# Evaluation Metrics\n## Metrics for Performance Assessment\n## Define relevant metrics\n## Compare ML performance against traditional methods\n# Case Studies and Applications\n## Lessons Learned\n# Challenges and Limitations\n## Technical Challenges\n## Model interpretability, scalability, and data availability\n## Ethical Considerations\n## Biases and ethical implications","[{\"question\":\"What problem does the thesis address for Elisa Oyj?\",\"answer\":\"It aims to improve Elisa’s use of private peering versus public peering or transit providers to reduce networking costs.\"},{\"question\":\"How does the proposed solution predict future traffic volumes?\",\"answer\":\"It develops a machine learning program that analyzes past traffic patterns from legacy data to forecast future traffic amounts between Elisa and peering partners.\"},{\"question\":\"Which main research questions does the thesis answer?\",\"answer\":\"It investigates whether legacy data can predict future trends and what factors must be considered to make good traffic prediction results.\"}]","Machine Learning–Based Traffic Prediction for Peering Selection in ISP Networks - 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