Mr. Gianfranco Speroni Profile

Gianfranco Speroni

at Univ Adolfo Ibáñez

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 25 July 2024 Presentation + Paper

Uncertainty estimation through quantile forest for prescriptive scheduling of data processing at ALMA

Rodrigo Carrasco, Luis Aburto, Jorge García Yus, Alfredo De Rodt, Gianfranco Speroni

Proceedings Volume 13098, 1309813 (2024) https://doi.org/10.1117/12.3019492

KEYWORDS: Data processing, Machine learning, Data modeling, Decision trees

Read Abstract +

The Atacama Large Millimeter/submillimeter Array (ALMA) is a prominent astronomical observatory known for its detailed imaging capabilities. Efficient scheduling of ALMA’s data processing tasks, especially those involving complex pipeline executions, is crucial for maximizing operational productivity. This paper addresses the challenge by developing a predictive model that estimates the runtime of these tasks, enabling more effective scheduling and resource management. Our approach employs the Light Gradient Boosting Machine (LGBM) and Quantile Forest models to predict processing times and quantify uncertainties. The use of these models is innovative, as it not only provides accurate predictions but also offers insights into the variability of processing times. This is particularly beneficial for handling the dynamic nature of the data processing workload at ALMA. We enhance the model’s performance and reliability by incorporating variable scaling and logarithmic transformations. To determine the best model, we comprehensively evaluated seven different machine-learning techniques. Our results show that the LGBM model and quantile estimation outperform traditional methods in predicting task durations. This leads to more efficient scheduling, as it allows the system to account for potential delays and optimize the sequencing of jobs. The quantile approach, in particular, offers a robust method for dealing with the inherent uncertainty in processing times. Our predictive tool has demonstrated a substantial reduction in overall flow time, decreasing it by 5.7%. Further improvements were achieved using stochastic scheduling techniques, which leverage the uncertainty estimates provided by our model. This research highlights the potential of machine learning to significantly enhance the operational efficiency of large-scale observatories like ALMA, providing a scalable and practical solution for managing complex data processing tasks.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years