Cloud Work Load Prediction through Different Models Based on Time-Series

被引：0

作者：

Caglar, Ilksen ^{[1
]}

Altilar, D. Turgay ^{[1
]}

机构：

[1] Istanbul Tech Univ, Comp Engn Dept, TR-34469 Istanbul, Turkey

来源：

2017 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2017年

关键词：

computational loads; time-series analysis; regression; Holt-Winters; Cloud; data centers;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Scheduling of computational load and actual processing is an important problem to be considered from the perspectives of time and consumed energy for execution in the scale of data centers. In this paper, time-series analysis of the arrivals of the workloads have been done by applying auto regression (AR), moving average (MA), auto regression and moving average (ARMA), and Holt-Winters approaches. Performances of the four methods was evaluated and compared for Google workload logs that is publicly available in the Internet.

引用

页码：856 / 860

页数：5

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