Wasserstein adversarial transformer for cloud workload prediction

S Arbat, VK Jayakumar, J Lee, W Wang… - Proceedings of the AAAI …, 2022 - ojs.aaai.org
Abstract Predictive VM (Virtual Machine) auto-scaling is a promising technique to optimize
cloud applications' operating costs and performance. Understanding the job arrival rate is …

An empirical analysis of vm startup times in public iaas clouds

J Hao, T Jiang, W Wang, IK Kim - 2021 IEEE 14th International …, 2021 - ieeexplore.ieee.org
VM startup time is an essential factor in designing elastic cloud applications. VM autoscaling
can reduce the under-/over-provisioning period of VMs with a precise estimation of VM …

Smartpick: Workload Prediction for Serverless-enabled Scalable Data Analytics Systems

AD Mohapatra, K Oh - Proceedings of the 24th International Middleware …, 2023 - dl.acm.org
Many data analytic systems have adopted a newly emerging compute resource, serverless
(SL), to handle data analytics queries in a timely and cost-efficient manner, ie, serverless …

[图书][B] The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions

M Scutari, M Malvestio - 2023 - taylorfrancis.com
Machine learning has redefined the way we work with data and is increasingly becoming an
indispensable part of everyday life. The Pragmatic Programmer for Machine Learning …

Predicting Cloud Performance Using Real-time VM-level Metrics

J Tian, A Elhabbash, Y Elkhatib - 2022 IEEE 24th Int Conf on …, 2022 - ieeexplore.ieee.org
The vast range of cloud service offerings can easily overwhelm users and cause them to
select ones that are unsuitable for their needs. As such, the literature has a number of …

Developing and Running Machine Learning Software: Machine Learning Operations (MLOps)

M Scutari, M Malvestio - Wiley StatsRef: Statistics Reference …, 2014 - Wiley Online Library
Abstract Machine learning software is fundamentally different from most other software in
one important respect: it is tightly linked with data. The behavior of machine learning …