A pilot review of the Google cluster workload trace 2019, methodology and its alternatives: analysis of workload in large scale data centres

Abstract

Cloud data centres require shared services that are highly available, elastic capacity, managed operations, and robust recovery capabilities to facilitate the next generation of efficient, reliable, and diverse connected computing environments. Nevertheless, large-scale cloud infrastructures continue to fail regularly despite their availability, scalability, and cost efficiency, primarily due to low resource utilisation and inadequate early-stage failure management. The key to effective resource management and minimizing failures in such settings is understanding the nature of the workload and its failure modes. The current review considers the Google cluster workload trace 2019 to investigate workload and failure patterns and to generalise the results of 24 articles. The analysis is also compared with other major datasets, such as Microsoft Azure Trace, Tencent Trace, and Alibaba Trace. The paper establishes the relevance of Google cluster traces, describes the key contents of the 2019 dataset, and contrasts prior literature with respect to research objectives, trace datasets, significant results, and limitations. Moreover, it briefly describes methods for analyzing and modeling cluster traces and identifies gaps in the research that should be addressed to advance the study of cluster traces.