Dominik Scheinert and Alexander Guttenberger, Technische Universit¨at Berlin, Germany

As distributed processing environments grow in complexity, accurate performance prediction models are essential to optimize system efficiency and resource allocation. However, modern computing workloads typically exhibit a wide variety of characteristics, which hinders optimized resource config-urations. Diverse approaches have been suggested to tackle the challenge of workload characterization, employing various parameters for performance modeling in the process. To expand on this objective, this paper extensively surveys existing performance modeling methodologies and introduces a 5+1 layer classifi-cation model designed to enhance the accuracy of predictive models by classifying and reflecting on relevant modeling parameters. We conducted a systematic literature review to identify and analyze the role of six key layers: Big Data Framework, Performance, Hardware, Data, User Application, and Virtualization. Our findings reveal that while the Big Data Framework and Performance Layers are foundational, predictive accuracy improves when combined with complementary layers, especially the Data Layer, which highlights the impact of data characteristics such as size and distribution. The Hardware Layer provides critical insights into system limitations, while the emerging Virtualization Layer reflects the increasing importance of virtualized, potentially cloud-based environments. The proposed 5+1 layer classification model offers a structured approach to capture and explain the complexity of distributed analytical workflows, providing a nuanced framework for performance modeling. This layered classification model aims to support the development of more robust, adaptable, and generalizable prediction models for use in cloud-based systems.

Big Data Analytics, Performance Modeling, Resource Management, Cloud Computing