Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

TL;DR

This paper systematically surveys and compares various machine learning models for cloud workload prediction, analyzing their performance on benchmark traces to identify strengths, weaknesses, and trade-offs in predictive resource management.

Contribution

It provides the first comprehensive classification, theoretical analysis, and experimental comparison of diverse machine learning workload prediction models in cloud computing.

Findings

Different models exhibit varying prediction accuracies and computational efficiencies.

Trade-offs between model complexity and prediction performance are identified.

Benchmark results highlight the most effective approaches for specific workload types.

Abstract

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.