cloud | UCSC OSPO

EnergyAPI: An End-to-End API for Energy-Aware Forecasting and Scheduling

Fri, 30 Jan 2026 00:00:00 +0000

Over the past decades, electricity demand has increased steadily, driven by structural shifts such as the electrification of transportation and, more recently, the rapid expansion of artificial intelligence (AI). Power grids have responded by expanding generation capacity, integrating renewable energy sources such as solar and wind, and deploying demand-response mechanisms. However, the current pace of demand growth is increasingly outstripping grid expansion, leading to integration delays, greater reliance on behind-the-meter consumption, and rising operational complexity.

To mitigate the environmental and socioeconomic impacts of electricity consumption, large consumers such as cloud data centers and electric vehicle (EV) charging infrastructures are increasingly participating in demand-response programs. These programs provide consumers with grid signals indicating favorable periods for electricity usage, such as when energy is cheapest or has the lowest carbon intensity. Consumers can then shift workloads across time and location to better align with grid conditions and their own operational constraints. A key challenge, however, is the online nature of this problem: operators must make real-time decisions without full knowledge of future grid conditions. While forecasting and optimization techniques exist, their effectiveness depends heavily on workload characteristics, such as whether tasks are delay-tolerant cloud jobs or EV charging sessions with route and deadline constraints.

This project proposes the design and implementation of a modular, extensible API for energy-aware workload scheduling. The API will ingest grid signals alongside workload Service Level Objectives (SLOs) and operational requirements, and produce execution plans that adapt to changing grid conditions. It will support multiple pluggable scheduling strategies and heuristics, enabling developers to compare real-time and forecast-based approaches across different workload classes. By providing a reusable, open-source interface for demand-response-aware scheduling, this project aims to lower the barrier for developers to integrate energy-aware decision-making into distributed systems and applications.

Building an End-to-End Service for Energy Forecasting and Scheduling

Topics: Databases Machine Learning
Skills: Python, command line tools (bash), SQL (MySQL or SQLite), FastAPI, time-series analysis, basic machine learning
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Abel Souza

Develop a containerized, end-to-end platform consisting of a backend, API, and web-based frontend for collecting, estimating, and visualizing real-time and forecasted electrical grid signals. These signals include electricity demand, prices, energy production, grid saturation, and carbon intensity. The system will support scalable data ingestion, region-specific forecasting models, and interactive visualizations to enable energy-aware application development and analysis.

Tasks:

Study electrical grid signals and demand-response data sources (e.g., demand, price, carbon intensity, grid saturation) and identify their requirements for real-time and forecast-based consumption planning.
Design and implement a relational data model for storing historical, real-time, and forecasted grid signals.
Ingest and validate grid signal data into a MySQL or SQLite database, ensuring data quality and time alignment across regions.
Implement baseline time-series forecasting models for grid signals (e.g., demand, price, or carbon intensity), with support for region-specific configurations.
Query European Network of Transmission System Operators for Electricity (ENTSO-E) and EIA (Energy Information Administration (EIA)) APIs to collect grid data.
Develop a RESTful API that exposes both raw and forecasted grid signals for use by energy-aware applications and schedulers.
Build a web-based user interface to visualize historical trends, forecasts, and regional differences in grid conditions.
Implement an interactive choropleth map to display spatial variations in grid signals such as carbon intensity and electricity prices.
Design an extensible architecture that allows different regions to plug in custom forecasting models or heuristics.
Containerize the backend, API, and frontend components using Docker to enable reproducible deployment and easy integration by external users.

Final Blog: Enhancing User Experience Reproducibility through TROVI Redesign

Wed, 18 Sep 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I worked on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program.

Before even starting this project, and me as a rising researcher, I always saw reproducibility as one of the biggest challenges in research. What I wanted to see was always as reproducibility—being able to consistently replicate experiments and share them in a way that others can follow.

TROVI, is a platform designed to help with this. However, as I joined the project, I knew it had room for improvement, not oly in the user interface, but also in the ease of integrating code and data.

This project aimed to address these challenges by redesigning TROVI to streamline experiment replication, making the platform more intuitive and accessible. The goal was simple: create a user-friendly experience that eliminates confusion and frustration, allowing researchers to focus on their work instead of the technical aspects of running a research experiment.

Our goals in the beginning of the summer:

We wanted to simplify TROVI’s interface for intuitive navigation, inspired by platforms like Google Colab.
We wanted to make uploading and sharing code and data easier, with seamless integration with tools like GitHub.
We wanted to create a mechanism for users to provide feedback, allowing TROVI to evolve based on real user needs.

How was the progress and what we have achieved

I started by conducting thorough UX research and a literature review on reproducibility platforms, establishing a solid foundation for the redesign. With user feedback guiding the process, I created wireframes and low-fidelity prototypes, focusing on making the platform more intuitive.

As the project progressed, I built a higher-fidelity prototype that connected various components of the platform, ensuring a seamless user journey. I then tackled the back-end integration, which tied together the front-end flows with TROVI’s API.

Throughout this project, I received valuable support and guidance from my mentors. Mark Powers walked me through TROVI’s architecture and helped me understand exactly what was needed for a successful redesign. Thanks to his mentorship, I not only completed the project but learned a great deal along the way. Thanks Mark Powers!!

Through iterations and feedback from initial user testing, and we the help of Kate Keahey, I refined the design to ensure it met the needs of the research community. By the end of the program, TROVI had evolved into a cohesive, user-friendly platform that leads to enhanced experiment reproducibility.

Accomplishments

A simplified interface that makes navigating, uploading, and collaborating much easier.
GitHub integration that streamlines the process of sharing code and data with collaborators.
A built-in feedback loop that enables TROVI to grow with its users, adapting to their needs as they arise.

The platform is also getting ready to move into production and will soon be available for the research community.

What’s Next?

While the core objectives have been successfully met, future improvements could further enhance the platform’s capabilities, such as additional integrations and more advanced collaboration features. User testing will continue to provide insights for ongoing development.

I’m grateful for this opportunity! Thank you for following along!

Trovi redesign process and low fidelity prototype in Figma

Mon, 22 Jul 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I’m working on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program. I’m excited to be working with two fabulous mentors, Kate Keahey, and Mark Powers. .

Research Reproducibility with a TROVI Redesign

As researchers, we constantly face challenges replicating experiments due to limitations in current tools. TROVI, a platform designed to facilitate experiment replication, can be hindered by hard-to-follow interfaces and difficulties integrating code and data. This leads to confusion and frustration.

My SoR project tackles these issues by redesigning TROVI to enhance user experience reproducibility. Imagine a user-friendly platform where uploading code, sharing data, and collaborating with colleagues becomes easy and straighforward.

The Redesign’s Goals

Enhanced User Experience: Inspired by user-friendly platforms like Google Colab, we’ll simplify TROVI’s interface for intuitive navigation and ease of use.
Uploads and Sharing: Uploading code and data, as well as collaborating with researchers, are key goals. Integration with platforms like GitHub will further streamline collaboration.
Continuous Improvement: A built-in feedback loop will allow users to provide input and suggestions, ensuring TROVI constantly evolves based on user needs.

Progress I have made so far

The first stage of my project began with conducting User Experience (UX) research and identifying user requirements for TROVI. I then conducted a literature review on reproducibility platforms to learn about efficient methodologies and platforms for reproducibility. This helped establish a clearer project scope. Additionally, I analyzed TROVI end-user feedback to understand redesign needs.

In summary, during the first weeks of the project, I focused on research and requirements gathering, including the literature review on state-of-the-art reproducibility platforms. Before midterm assessment, my work also involved the redesign process, prioritizing improved usability and user experience. I designed wireframes following requierements and user feedback and later translated them into a low-fidelity prototypes. Front-end and back-end considerations were made, such as selecting a front-end language (Vue.js) and a collaborative design tool (Figma).

What do I plan to do over the next weeks?

During the next two weeks, I will address challenges encountered in the design process and make the necessary adjustments to ensure the success of the next steps of the project. A higher-fidelity prototype will be completed, including connections between the different objects and frames. This will facilitate the creation of a front-end with multiple flows in the prototype. Additionally, this will provide a preview of the end-user experience through the design process, without requiring the back-end to be functional or connected yet. I’m also investigating design tool API integrations to access TROVI’s APIs. This will give us the ability to access and isolate any TROVI artifact properties associated with it.

I’m halfway in the redesign process. Next steps will include the integration of both the backend and frontend components to create a cohesive and functional system. We will also facilitate initial user interactions and testing to gather valuable feedback and ensure that the system meets the needs and expectations of end users.
In addition, as I progress, my focus will shift towards enhancing the user experience and refining the final product based on the feedback received. The final two weeks of the program will be dedicated to this critical phase, where I will implement user experience techniques and conduct thorough testing to polish the product. This period will involve close analysis and iteration to address any issues, and an optimize functionality.
By the end of the program, I aim to deliver a functional and user-friendly product that not only meets the initial project goals but also exceeds user expectations.

Stay tuned to see how TROVI is built for reproducible research!!

First Steps in Enhancing User Experience Reproducibility through TROVI Redesign

Wed, 12 Jun 2024 00:00:00 +0000

Hello! My name is Alicia Esquivel Morel, and I’m a graduate research assistant at the University of Missouri – Columbia, pursuing a PhD in Computer Science. This summer, I’m working on a project to improve user experience reproducibility through a redesign of TROVI, as part of the Summer of Reproducibility (SoR) program. Excited to be working with two fabulous mentors; Kate Keahey, and Mark Powers.

Research Reproducibility with a TROVI Redesign

Researchers constantly face challenges replicating experiments due to limitations in current tools. TROVI, a platform designed to facilitate experiment replication, can be hindered by hard to follow interfaces and difficulties integrating code and data. This leads to confusion and frustration.

The Redesign’s Goals

Enhanced User Experience: Inspired by user-friendly platforms like Google Colab, we’ll simplify TROVI’s interface for intuitive navigation and ease of use.
Uploads and Sharing: Uploading code and data, as well as collaborating with researchers are key goals. Integration with platforms like GitHub will further streamline collaboration.
Continuous Improvement: A built-in feedback loop will allow users to provide input and suggestions, ensuring TROVI constantly evolves based on user needs.

The Road Ahead

We’re at the beginning of the redesign process. In the next blog post, I’ll describe the project’s specific goals and the deliverables you can expect.

Stay tuned to see how TROVI is built for reproducible research!!