Resource Allocation Influence on Application Performance in Sliced Testbeds

TL;DR

This paper investigates how resource allocation affects application performance in network slices within testbeds, highlighting the non-uniform impact of CPU and memory on slicing latency in modern network architectures.

Contribution

It provides empirical insights into resource allocation effects on network slice performance using experimental analysis in real-world testbeds.

Findings

Different resource impacts observed across testbeds

CPU and memory influence slicing latency non-uniformly

Resource allocation affects network slice performance

Abstract

Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives this evolution, along with research into Beyond 5G (B5G) and 6G architectures. Network slices require seamless management, observability, and intelligent-native resource allocation, considering user satisfaction, cost efficiency, security, and energy. Slicing orchestration architectures have been extensively studied to accommodate these requirements, particularly in resource allocation for network slices. This study explored the observability of resource allocation regarding network slice performance in two nationwide testbeds. We examined their allocation effects on slicing connectivity latency using a partial factorial experimental method with Central Processing Unit (CPU) and memory combinations. The results reveal different resource impacts across the testbeds, indicating a non-uniform influence on the CPU and memory within the same network slice.