Brewing Analytics Quality for Cloud Performance

TL;DR

This paper introduces a comprehensive software process for assessing and ensuring the quality of cloud performance data, integrating diverse data sources and validating data processing to improve cloud deployment decisions.

Contribution

It presents an innovative, multi-layered process for evaluating cloud performance data quality, combining various data types and addressing data tidiness challenges.

Findings

Effective data quality assessment process demonstrated in case study

Improved reliability of performance data for cloud deployment decisions

Framework for validating data processing procedures

Abstract

Cloud computing has become increasingly popular. Many options of cloud deployments are available. Testing cloud performance would enable us to choose a cloud deployment based on the requirements. In this paper, we present an innovative process, implemented in software, to allow us to assess the quality of the cloud performance data. The process combines performance data from multiple machines, spanning across user experience data, workload performance metrics, and readily available system performance data. Furthermore, we discuss the major challenges of bringing raw data into tidy data formats in order to enable subsequent analysis, and describe how our process has several layers of assessment to validate the quality of the data processing procedure. We present a case study to demonstrate the effectiveness of our proposed process, and conclude our paper with several future research directions worth investigating.