ANALISIS PERFORMANCE VIRTUAL MACHINE SERVER DAN MITIGASI MENGGUNAKAN NAÏVE BAYES CLASSIFICATION

HIDAYAT, TAUFIK (2018) ANALISIS PERFORMANCE VIRTUAL MACHINE SERVER DAN MITIGASI MENGGUNAKAN NAÏVE BAYES CLASSIFICATION. S2 thesis, Universitas Mercu Buana Jakarta-Menteng.

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Abstract

Evolusi virtualisasi di pusat data cepat dan sedang banyak digunakan di dunia. Penggunaan virtual machine (VM) di data center (DC) tidak dapat dikesampingkan dari masalah seperti: Masalah System Operasi (OS), jaringan virtual, memori dan penggunaan CPU. Selain masalah tersebut, pemanfaatan VM dapat ditemukan dalam teknologi cloud computing untuk membuat lebih efisien dan kinerjanya harus baik sepenuhnya. Masalah dalam VM sangat kompleks. Ini dapat ditemukan di OS, aplikasi dan server fisik. Agar tetap dalam kinerja yang baik, operator operasional harus mengoperasikan sistem pemantauan VM untuk menjaga VM berjalan dengan baik. Penelitian ini akan menggunakan beberapa metode fuzzy Mamdani, holdout validation dan Naïve Bayes Classification. Hasil Prediksi untuk rekomendasi migrasi VM dengan Naïve Bayes Classification pada masalah workload VM dapat diprediksi dengan tingkat accuracy 99,12 %, Recall 97,87 % dan Precision 100%. Keywords: virtual machine, data center, fuzzy Mamdani, holdout validation, naïve bayes classification. Evolution of virtualization in data center is fast and is being widely used in the world. Usage of virtual machine (VM) in data center (DC) cannot be aside from problems such as: Operating System (OS) problem, virtual network, memory and CPU utilizations. Besides of those problems, utilization of VM can be found in cloud computing technology to make more efficient and its performance should be good entirely. Problem in VM is very complex. It can be found in OS, application and physical server. To keep it in good performance, operational operator should operate VM monitoring system in order to keep VM run well. This research will use several methods, such as: fuzzy Mamdani, holdout validation and Naïve Bayes Classification. These methods will then create decision making for VM performance condition. Result Prediction for VM migration recommendation with Naïve Bayes Classification on VM workload problems can be predicted with a 99.12% accuracy rate, Recall 97.87 % and 100% Precision. Keywords: virtual machine, data center, fuzzy Mamdani, holdout validation, naïve Bayes.

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