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Energy Profiling of Data-Sharing Pipelines: Modeling, Estimation, and Reuse Strategies

Sepideh Masoudi, Sebastian Werner, Pierluigi Plebani, Stefan Tai

TL;DR

The paper tackles energy inefficiency in cross-organizational data-sharing pipelines by introducing a formal energy profiling framework that models stage-level energy consumption and data-volume transformations. It decomposes energy into operational, transmission, and monitoring components and defines a calculable total energy, enabling estimation across multiple pipeline configurations and the identification of reusable common stages. A preliminary simulation demonstrates the feasibility of the approach and its potential to reduce energy waste through reuse and strategic offloading, while acknowledging governance constraints and the need for real-world validation. Future work aims to enhance energy estimation with machine learning and to apply the framework within real federated data-sharing contexts like TEADAL.

Abstract

Data-sharing pipelines involve a series of stages that apply policy-based data transformations to enable secure and effective data exchange among organizations. Although numerous tools and platforms exist to manage governance and enforcement in these pipelines, energy efficiency in data exchange has received limited attention. This paper introduces a novel method to model and estimate the energy consumption of different execution configurations in data-sharing pipelines. Additionally, this method identifies reuse potential in shared stages across pipelines that hold the key to reducing energy in large data-sharing federations. We validate this method through simulation experiments, revealing promising potential for cross-organizational pipeline optimization and laying a foundation for energy-conscious execution strategies.

Energy Profiling of Data-Sharing Pipelines: Modeling, Estimation, and Reuse Strategies

TL;DR

The paper tackles energy inefficiency in cross-organizational data-sharing pipelines by introducing a formal energy profiling framework that models stage-level energy consumption and data-volume transformations. It decomposes energy into operational, transmission, and monitoring components and defines a calculable total energy, enabling estimation across multiple pipeline configurations and the identification of reusable common stages. A preliminary simulation demonstrates the feasibility of the approach and its potential to reduce energy waste through reuse and strategic offloading, while acknowledging governance constraints and the need for real-world validation. Future work aims to enhance energy estimation with machine learning and to apply the framework within real federated data-sharing contexts like TEADAL.

Abstract

Data-sharing pipelines involve a series of stages that apply policy-based data transformations to enable secure and effective data exchange among organizations. Although numerous tools and platforms exist to manage governance and enforcement in these pipelines, energy efficiency in data exchange has received limited attention. This paper introduces a novel method to model and estimate the energy consumption of different execution configurations in data-sharing pipelines. Additionally, this method identifies reuse potential in shared stages across pipelines that hold the key to reducing energy in large data-sharing federations. We validate this method through simulation experiments, revealing promising potential for cross-organizational pipeline optimization and laying a foundation for energy-conscious execution strategies.

Paper Structure

This paper contains 10 sections, 5 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Comprehensive energy profiling
  4. Problem Statement
  5. Modeling the Energy of data-sharing Pipelines
  6. Energy Profiling
  7. Evaluation
  8. Limitations
  9. Conclusions
  10. Acknowledgments.

Figures (3)

  • Figure 1: Different execution configurations of two data-sharing pipelines.
  • Figure 2: Data flow diagram of energy profiling in data-share pipelines.
  • Figure 3: Aggregation of stages across different pipelines in a single simulation round, with 100 pipeline populations created from random combinations of 11 stages from 4 types(filter, anonymization, aggregation, convert).