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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Data on performance prediction for cloud service selection.

Abdullah Mohammed Al-Faifi¹, Biao Song¹, Mohammad Mehedi Hassan¹

¹College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia.

|September 19, 2018

Summary

This summary is machine-generated.

This study provides data for predicting cloud service performance by analyzing workload parameters. Key metrics like memory and CPU utilization, and response time, are detailed for informed cloud selection.

Keywords:

Cloud computing Performance metrics Workload parameters

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Area of Science:

Computer Science
Cloud Computing
Data Science

Background:

Cloud service selection relies on performance metrics.
Workload parameters significantly influence system performance.
Data-driven approaches are crucial for optimizing cloud resource allocation.

Purpose of the Study:

To present a comprehensive dataset for performance prediction in cloud service selection.
To identify and analyze key workload parameters affecting cloud performance.
To provide data for developing automated performance prediction models.

Main Methods:

Collected 28,147 instances from 13 cloud nodes at the KSA Ministry of Finance.
Recorded data over a continuous period from March 1, 2016, to February 20, 2017.
Selected 9 workload parameters (e.g., Number of Jobs, Memory Capacity, CPU Cores) and 3 performance metrics (e.g., Memory utilization, CPU utilization, response time).

Main Results:

The dataset includes detailed measurements of selected workload parameters and performance metrics.
The data spans a significant period, offering temporal insights into cloud performance.
This data supports the analysis of relationships between workload characteristics and service performance.

Conclusions:

The presented data is valuable for research on automated performance prediction models for cloud services.
Understanding workload parameters is essential for accurate cloud performance evaluation.
This dataset facilitates advancements in smart data utilization for cloud service selection.