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.

翻译：云服务的快速扩张及其不可预测的工作负载需求给资源管理带来了重大挑战。传统资源管理方法主要基于静态规则和阈值，往往难以确保成本效益和最优资源利用。本研究引入了一种旨在预测流量需求的预测模型，力求从被动式资源管理转向主动式资源管理方法。通过将先进的预测分析能力与P4可编程交换机的功能相结合，本研究旨在提升资源利用效率并增强系统鲁棒性。其目标是为组织提供有效应对动态云环境复杂性所需的敏捷性与经济效益。该方法不仅有望优化微服务资源分配，同时支持构建更具弹性与高效云基础设施的宏观目标。