CSI2: Cloud Server Idleness Identification by Advanced Machine Learning in Theories and Practice

Duan, Jun; Li, Guangcheng; Asthana, Neeraj; Zeng, Saixing; Dell’Era, Ivan; Chanana, Aman; Agastya, Chitra; Pointer, W. D.; Ren, Yan

doi:10.1007/978-3-030-33702-5_19

Cited by 1 publication

(1 citation statement)

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“…However, their common challenge is the utilization of excessively conservative thresholds, leading to the oversight of a significant number of non-productive workloads. Recently, Duan et al employed a supervised learning technique, constructing a binary classifier, to distinguish non-productive workloads from productive ones (Duan et al 2019). However, it requires extensive manually tagged data, which can be timeconsuming and labor-intensive to acquire.…”

Section: Introductionmentioning

confidence: 99%

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization

Yang,

Arora,

Jha

et al. 2024

AAAI

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The widespread adoption of public and hybrid clouds, along with elastic resources and various automation tools for dynamic deployment, has accelerated the rapid provisioning of compute resources as needed. Despite these advancements, numerous resources persist unnecessarily due to factors such as poor digital hygiene, risk aversion, or the absence of effective tools, resulting in substantial costs and energy consumption. Existing threshold-based techniques prove inadequate in effectively addressing this challenge. To address this issue, we propose an unsupervised machine learning framework to automatically identify resources that can be de-provisioned completely or summoned on a schedule. Application of this approach to enterprise data has yielded promising initial results, facilitating the segregation of productive workloads with recurring demands from non-productive ones.

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Section: Introductionmentioning

confidence: 99%