The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.

翻译：由于异构服务类型和动态工作负载的高度可变性与高维度性，资源使用的精确预估是一项复杂且具有挑战性的问题。在过去几年中，资源使用与流量的预测已引起研究界的广泛关注。许多基于机器学习的工作负载预测模型，通过利用其计算能力与学习能力得以开发。本文首次对多样化的机器学习驱动的云工作负载预测模型进行了系统性的综述与基于性能分析的比较研究。讨论从预测性资源管理的重要性展开，随后对这些工作负载预测模型进行了框架描述、运作设计、动机阐述及挑战分析。本文基于现有最优工作负载预测方法的理论概念与数学运作机制，将不同预测方法划分为五个不同类别，并进行了分类学阐述。针对属于不同机器学习模型类别的几种最突出的预测方法，进行了全面的梳理与比较。所有五类已分类的基于机器学习的工作负载预测模型均在一个通用平台上实现，并利用三个不同的标准云工作负载轨迹通过实验分析进行系统性研究与比较。对现有最优方法的关键性能指标进行了评估以进行比较，最后通过讨论其权衡与值得注意的要点对本文进行了总结。