Finecloud: Fine-grained cloud service advisory using machine learning

Name: Finecloud: Fine-grained cloud service advisory using machine learning
Author: Orhun, Yasemin, İstanbullu, Yiğit, Arı, İsmail

İsim	Finecloud: Fine-grained cloud service advisory using machine learning
Yazar	Orhun, Yasemin, İstanbullu, Yiğit, Arı, İsmail
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Application services, Cloud monitoring, DTU, LSTM, Machine learning, OPEX, Time-series, Virtual machine
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166548045-1
Kayıt Numarası	09aa7904-5fd5-4ab5-b078-4d6ab2b4c49a
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as forgetting about unused resources, bursty workloads and service dependencies causing under-utilization (a.k.a. over- provisioning) problem. Cloud advisory tools offered by the public providers either lack the fine-grained analysis needed for actionable recommendations or can't see the correlations among services that are used by the same customers' resource groups. We proposed an automated, near real-time advisor that utilizes historical usage data and machine learning (ML) models to recommend cost saving opportunities. We demonstrated significant cost savings averaging around 20%, which can accumulate as thousands of Dollars for large and active systems. Since our advisory models depend on time-series data, we compared several forecasting algorithms including ARIMA, LSTM and Prophet. We found LSTM model to deliver the most accurate results for our workloads.
DOI	10.1109/BigData55660.2022.10020934

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Finecloud: Fine-grained cloud service advisory using machine learning

Yazar Orhun, Yasemin, İstanbullu, Yiğit, Arı, İsmail

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Application services, Cloud monitoring, DTU, LSTM, Machine learning, OPEX, Time-series, Virtual machine

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166548045-1

Kayıt Numarası 09aa7904-5fd5-4ab5-b078-4d6ab2b4c49a

Lokasyon Computer Science

Tarih 2022

Örnek Metin Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as forgetting about unused resources, bursty workloads and service dependencies causing under-utilization (a.k.a. over- provisioning) problem. Cloud advisory tools offered by the public providers either lack the fine-grained analysis needed for actionable recommendations or can't see the correlations among services that are used by the same customers' resource groups. We proposed an automated, near real-time advisor that utilizes historical usage data and machine learning (ML) models to recommend cost saving opportunities. We demonstrated significant cost savings averaging around 20%, which can accumulate as thousands of Dollars for large and active systems. Since our advisory models depend on time-series data, we compared several forecasting algorithms including ARIMA, LSTM and Prophet. We found LSTM model to deliver the most accurate results for our workloads.

DOI 10.1109/BigData55660.2022.10020934