SLA-Centric Automated Algorithm Selection Framework for Cloud Environments
This addresses SLA management for cloud service providers and consumers, but appears incremental as it applies existing ML techniques to a specific domain problem.
The authors tackled the problem of SLA violations in cloud computing by proposing an automated algorithm-selection framework that uses machine learning to rank algorithm-hardware pairs based on SLA constraints, applying it to the 0-1 knapsack problem with a curated dataset of 6 algorithms.
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.