Using Neural Networks on Cloud Container’s Performance Comparison By R on Docker (ROCKER)

Gumelar, Agustinus Bimo; Lusia, Dwi Ayu; Widodo, Agung; Felani, Rendra

doi:10.1109/sain.2018.8673356

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…These applications apply as the dataset processing media and monitoring for each of hardware performance while in different states. This step is done using various monitoring tools, namely HTOP, Docker stats, df, and Cockpit [18], [19]. Each hardware device can use the same monitoring tool due to the tool being able to read much information contained in the server's hardware.…”

Section: Flow Of the Experimentsmentioning

confidence: 99%

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Optimizing Virtual Resources Management Using Docker on Cloud Applications

Felani

Al-Azam

Adi

et al. 2020

Indonesian J. Comput. Cybern. Syst.

Self Cite

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This study aims to optimize servers with low utility levels on hardware using container virtualization techniques from Docker. This study's primary focus is to maximize the work of the CPU, RAM, and Hard Drive. The application of virtualization techniques is to create many containers as each of the containers is for the application to run a cloud storage system with the CaaS service infrastructure concept (Container as a Service). Containers on infrastructure will interact with other containers using configuration commands at Docker to form an infrastructure service such as CaaS in general. Testing of hardware carried out by running five Nextcloud cloud storage applications and five MariaDB database applications running in Docker containers and tested by random testing using a multimedia dataset. Random testing with datasets includes uploading and downloading datasets simultaneously and CPU monitoring under load, RAM, and Disk hardware resources. The testing will be done using Docker stats, HTOP, and Cockpit monitoring tools to determine the hardware capabilities when processing multimedia datasets.

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Section: Flow Of the Experimentsmentioning

confidence: 99%

“…This functionality is present in the Cockpit Docker package. Cockpit communicates with Docker daemons via the API via socket /var/run/docker.sock UNIX [18]. By doing this, we will be able to determine the server's ability to process multimedia datasets.…”

Section: Multimedia Datasetmentioning

confidence: 99%

Optimizing Virtual Resources Management Using Docker on Cloud Applications

Felani

Al-Azam

Adi

et al. 2020

Indonesian J. Comput. Cybern. Syst.

Self Cite

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An Anatomy of Machine Learning Data Visualization

Gumelar

2019

2019 International Seminar on Application for Technology of Information and Communication (iSemantic)

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Developing an Integrated Smart Model to Enhance the Efficacy of Stock Market Prediction by Leveraging XGBoost and Long Short-Term Memory Networks

Goen

2023

IJTBM

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A well-known economic tactic, the stock exchange has emerged as a crucial testing ground for the rapidly developing science of machine learning (ML). Stock prices can be predicted by using machine learning (ML) to analyse several aspects of the behaviour of the stock market. Given that stock prices are dynamic and influenced by real-time events, they cannot be predicted. However, deep learning algorithms can easily handle intricate data given in different patterns of stock prices.

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Using Neural Networks on Cloud Container’s Performance Comparison By R on Docker (ROCKER)

Cited by 3 publications

References 6 publications

Optimizing Virtual Resources Management Using Docker on Cloud Applications

Optimizing Virtual Resources Management Using Docker on Cloud Applications

An Anatomy of Machine Learning Data Visualization

Developing an Integrated Smart Model to Enhance the Efficacy of Stock Market Prediction by Leveraging XGBoost and Long Short-Term Memory Networks

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