Skripsi

PREDIKSI SISTEM AUTO-SCALING PADA CLOUD PRIVATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

---

Penilaian

---

Cloud computing offers flexibility in managing IT resources, particularly through auto-scaling mechanisms that automatically adjust capacity based on workload. In private cloud environments, auto-scaling plays a crucial role in maintaining service availability while minimizing resource waste. However, its effectiveness relies heavily on the system’s ability to accurately predict future workloads. This study aims to evaluate three time series forecasting models—ARIMA, Holt’s Linear Trend, and Simple Exponential Smoothing (SES)—in predicting CPU usage, RAM usage, and disk activity in a virtualized private cloud system. Historical resource data were analyzed to identify usage patterns. Results indicate that CPU and disk activity data exhibit fluctuating and non-stationary behavior, while RAM usage is relatively stable. The models were evaluated using RMSE, MAE, and MAPE metrics. Evaluation results show that the ARIMA model consistently yields the lowest prediction errors across all datasets. With its higher accuracy compared to the other models, ARIMA is recommended as the primary model to support efficient, responsive, and adaptive auto-scaling in private cloud environments. It effectively captures trend and seasonal patterns in data, enabling better resource allocation, reducing overprovisioning, and avoiding underutilization.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005485	T183163	T1831632025	Central Library (Referensi)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1831632025

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 175 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment