Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning

TL;DR

This paper reviews black-box predictive performance modeling techniques for distributed computing, emphasizing machine learning approaches that estimate job performance metrics without workload modification, addressing challenges in scheduling and resource management.

Contribution

It provides a comprehensive classification and comparison of non-intrusive performance prediction methods and highlights open research challenges in the field.

Findings

Non-intrusive methods can predict performance metrics effectively.

Various machine learning techniques are applicable for performance modeling.

Identified key open problems and future research directions.

Abstract

In many domains, the previous decade was characterized by increasing data volumes and growing complexity of computational workloads, creating new demands for highly data-parallel computing in distributed systems. Effective operation of these systems is challenging when facing uncertainties about the performance of jobs and tasks under varying resource configurations, e.g., for scheduling and resource allocation. We survey predictive performance modeling (PPM) approaches to estimate performance metrics such as execution duration, required memory or wait times of future jobs and tasks based on past performance observations. We focus on non-intrusive methods, i.e., methods that can be applied to any workload without modification, since the workload is usually a black-box from the perspective of the systems managing the computational infrastructure. We classify and compare sources of performance variation, predicted performance metrics, required training data, use cases, and the underlying prediction techniques. We conclude by identifying several open problems and pressing research needs in the field.