Traffic Generation for Benchmarking Data Centre Networks

TL;DR

This paper introduces TrafPy, an open-source traffic generator for benchmarking data centre networks, enabling standardized testing and analysis of scheduling algorithms across various network types.

Contribution

The paper presents TrafPy, a versatile framework for generating realistic data centre traffic traces, facilitating benchmarking and analysis of network scheduling algorithms.

Findings

Sensitivity of scheduling algorithms to traffic characteristics analyzed

Different network types respond uniquely to scheduling policies

Insights enable application-informed design and adaptive scheduling

Abstract

Benchmarking is commonly used in research fields, such as computer architecture design and machine learning, as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre networking community lacks a standard open-access benchmark. This is curtailing the community's understanding of existing systems and hindering the ability with which novel technologies can be developed, compared, and tested. We present TrafPy; an open-access framework for generating both realistic and custom data centre network traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. To demonstrate the efficacy of TrafPy, we use it to conduct a thorough investigation into the sensitivity of 4 canonical scheduling algorithms (shortest remaining processing time, fair share, first fit, and random) to varying traffic trace characteristics. We show how the fundamental scheduler performance insights revealed by these tests translate to 4 realistic data centre network types; University, Private Enterprise, Commercial Cloud, and Social Media Cloud. We then draw conclusions as to which types of scheduling policies are most suited to which types of network load conditions and traffic characteristics, leading to the possibility of application-informed decision making at the design stage and new dynamically adaptable scheduling policies. TrafPy is open-sourced via GitHub and all data associated with this manuscript via RDR.