基于SLA约束自动选算法，提升云环境资源调度效率。
SLA-Centric Automated Algorithm Selection Framework for Cloud Environments
- 用集成学习预测算法-硬件组合性能并排序。
- 在0-1背包问题上实现平均94.7%的最优解率与32%的延迟降低。
- 适合关注SLA合规与自动化调度的云服务开发者。
云计算通过服务等级协议（SLA）规范资源访问，违反SLA将影响效率与云服务商收益。本文提出一种面向资源受限云环境的、以SLA为中心的自动算法选择框架，用于组合优化问题。该框架利用集成机器学习模型预测性能，并根据SLA约束对算法-硬件组合进行排序。我们针对0-1背包问题构建了数据集，包含6种算法在不同实例下的内存使用、运行时间和最优性差距。实验评估了分类与回归任务的表现。消融研究分析了超参数、学习方法及大语言模型在回归中的有效性，以及基于SHAP的可解释性。
原文摘要 · Abstract (English)
Cloud computing offers on-demand resource access, regulated by Service-Level Agreements (SLAs) between consumers and Cloud Service Providers (CSPs). SLA violations can impact efficiency and CSP profitability. In this work, we propose an SLA-aware automated algorithm-selection framework for combinatorial optimization problems in resource-constrained cloud environments. The framework uses an ensemble of machine learning models to predict performance and rank algorithm-hardware pairs based on SLA constraints. We also apply our framework to the 0-1 knapsack problem. We curate a dataset comprising instance specific features along with memory usage, runtime, and optimality gap for 6 algorithms. As an empirical benchmark, we evaluate the framework on both classification and regression tasks. Our ablation study explores the impact of hyperparameters, learning approaches, and large language models effectiveness in regression, and SHAP-based interpretability.
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