Workload Prediction in P4 Programmable Switches

TL;DR

This paper presents a predictive workload model integrated with P4 programmable switches to enable proactive resource management, improving efficiency and robustness in cloud environments.

Contribution

It introduces a novel predictive analytics approach tailored for P4 switches to enhance cloud resource management.

Findings

Improved workload forecasting accuracy

Enhanced resource utilization efficiency

Increased system robustness

Abstract

The rapid expansion of cloud services and their unpredictable workload demands present significant challenges in resource management. Traditional resource management approaches, primarily based on static rules and thresholds, often fail to ensure cost-effectiveness and optimal resource utilization. This research introduces a predictive model designed to forecast traffic demand, aiming to shift from a reactive to a proactive resource management approach. By integrating advanced predictive analytics with the capabilities of P4 programmable switches, this study seeks to enhance the efficiency of resource utilization and improve system robustness. The goal is to equip organizations with the agility and economic efficiency required to navigate the complexities of dynamic cloud environments effectively. This approach not only promises to refine microservice resource allocation but also supports the broader objective of fostering more resilient and efficient cloud infrastructures.