FinOps : Monitoring and Controlling GCP costs
Ait Chikh, Saad (2023)
Ait Chikh, Saad
2023
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023061555190
https://urn.fi/URN:NBN:fi-fe2023061555190
Tiivistelmä
Cloud computing has gained significant popularity in today’s digital landscape, with companies relying on cloud-based solutions to manage their data, applications, and infrastructure. The cloud offers several advantages, including scalability, flexibility, and cost-effectiveness, making it a popular choice for businesses of all sizes. However, with the increasing adoption of cloud technologies, it is important for companies to keep a close eye on their cloud usage costs to ensure they are using the cloud efficiently and effectively. This is where the discipline of Financial Operations (FinOps) comes into play. FinOps seeks to optimize cloud spending, and it has become increasingly important for organizations that utilize cloud computing. By implementing FinOps practices, companies can achieve better cost visibility and control, leading to more efficient and effective cloud usage. While several cloud providers are available in the market, such as Amazon Web Services (AWS) and Microsoft Azure, this work will focus specifically on Google Cloud Platform (GCP).
The goal of this thesis is to present two implemented solutions for managing GCP costs: proactive anomaly detection and cost forecasting using machine learning (ML) algorithms. Thanks to anomaly detection, companies can detect unusual patterns in their cloud billing data and proactively alert teams to investigate and address any issues. Furthermore, forecasting future costs can help companies anticipate potential cost spikes and take proactive measures to avoid them.
The goal of this thesis is to present two implemented solutions for managing GCP costs: proactive anomaly detection and cost forecasting using machine learning (ML) algorithms. Thanks to anomaly detection, companies can detect unusual patterns in their cloud billing data and proactively alert teams to investigate and address any issues. Furthermore, forecasting future costs can help companies anticipate potential cost spikes and take proactive measures to avoid them.