Workload Characterization and Empirical Taxonomy: Dominance of Read/ Write Orientation and Homogeneity of Secondary Behavioral Metrics
Yue Yong, Olaniyi Olawale Omoyajowo
Journal of Information & Systems Management, Vol. 16, No. 3, 2026.
DOI: https://doi.org/10.6025/jism/2026/16/3/110-125
Link: https://www.dline.info/jism/fulltext/v16n3/jismv16n3_2.pdf
Abstract:
Effective workload characterization is essential for resource management in cloud data centres, yet the
relative importance of different workload descriptors remains unclear. This study analyses a corpus of
15,000 workload observations to identify the dominant empirical dimensions of workload variation and to
evaluate whether secondary behavioural metrics provide independent discriminatory information beyond
read/write orientation. Observations were classified into read heavy (74.77%), mixed (25.09%), and writeheavy
(0.14%) categories. We further cross classified a six type workload taxonomy with four data modalities
to examine finer descriptive structure. Principal Component Analysis followed by silhouette optimised Kmeans
clustering (K = 2, silhouette = 0.1957) partitioned the feature space into two groups distinguished
almost exclusively by read/write composition, while request rate, hotspot intensity, access skew, data
locality, and workload change rate remained virtually identical across clusters. Kruskal Wallis testing
confirmed that read and write ratios differed highly significantly among workload classes (H = 8493.179, p
< .001), whereas all secondary metrics were statistically homogeneous (p > .20). The findings demonstrate
that read/write orientation constitutes the principal organising axis of workload behaviour in this corpus,
while secondary characteristics do not form independent classification dimensions. The results support a
parsimonious, hierarchical workload representation in which read/write ratio serves as the primary
classification feature and secondary metrics function as continuous operational descriptors. This paper
discusses implications for workload aware database selection, cloud native architecture, and resource
management frameworks.