CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads

M Hossain, D Mebratu, N Hasabnis, J Jin… - arXiv preprint arXiv …, 2022 - arxiv.org
Workloads in modern cloud data centers are becoming increasingly complex. The number of
workloads running in cloud data centers has been growing exponentially for the last few …

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization

X Yang, RR Arora, S Jha, C Narayanaswami… - Proceedings of the …, 2024 - ojs.aaai.org
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 …

[HTML][HTML] A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning

M Garralda-Barrio, C Eiras-Franco… - Journal of Parallel and …, 2024 - Elsevier
Comprehensive workload characterization plays a pivotal role in comprehending Spark
applications, as it enables the analysis of diverse aspects and behaviors. This …

On the Implications of Heterogeneous Memory Tiering on Spark In-Memory Analytics

M Katsaragakis, D Masouros… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
Today, the rise of big data has driven a growing demand for efficient and scalable computing
solutions that can handle the massive amounts of data generated by modern applications …

[图书][B] Dynamic Object Partitioning and Replication for Cooperative Cache

O Asad - 2021 - search.proquest.com
Data intensive applications are usually designed using a multi-tier architecture that
comprises a web tier, an application tier and a backend database tier. To process requests …