Data analysis of cloud virtualization experiments

TL;DR

This paper presents a dataset and analysis of how different virtualization technologies and network parameters affect end-to-end latency in cloud environments, aiding in performance prediction and management.

Contribution

It introduces a comprehensive dataset of network measurements under various virtualization and network conditions, and demonstrates its use in modeling and visualizing latency impacts.

Findings

Virtualization impacts network latency significantly.

Data models can predict packet round-trip time.

Visualization techniques reveal key latency factors.

Abstract

The cloud computing paradigm underlines data center and telecommunication infrastructure design. Heavily leveraging virtualization, it slices hardware and software resources into smaller software units for greater flexibility of manipulation. Given the considerable benefits, several virtualization forms, with varying processing and communication overheads, emerged, including Full Virtualization and OS Virtualization. As a result, predicting packet throughput at the data plane turns out to be more challenging due to the additional virtualization overhead located at CPU, I/O, and network resources. This research presents a dataset of active network measurements data collected while varying various network parameters, including CPU affinity, frequency of echo packet injection, type of virtual network driver, use of CPU, I/O, or network load, and the number of concurrent VMs. The virtualization technologies used in the study include KVM, LXC, and Docker. The work examines their impact on a key network metric, namely, end-to-end latency. Also, it builds data models to evaluate the impact of a cloud computing environment on packet round-trip time. To explore data visualization, the dataset was submitted to pre-processing, correlation analysis, dimensionality reduction, and clustering. In addition, this paper provides a brief analysis of the dataset, demonstrating its use in developing machine learning-based systems for administrator decision-making.