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Tactic: Use Energy-Aware Scheduling

Tactic sort: Awesome Tactic

Type: Architectural Tactic

Category: green-ml-enabled-systems

Tags: deployment machine-learning measured

Title

Use Energy-Aware Scheduling

Description

Energy-aware scheduling refers to a strategy that optimizes the scheduling of machine learning tasks. It dynamically schedules tasks or processes based on the current energy requirements and system conditions. The objective of an energy-aware dynamic scheduling policy is to make efficient use of available computational resources.

Participant

Software Designer

Related software artifact

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Context

Machine Learning

Software feature

< unknown >

Tactic intent

Improve energy efficiency by dynamically managing workers to maximize the overall utilization in distributed systems

Target quality attribute

Resource Utilization

Other related quality attributes

Performance, Energy Efficiency

Measured impact

Sun et al show that energy-aware scheduling schedules 6 % more workers compared to other methods.

Source

Yuxuan Sun, Sheng Zhou, and Deniz Gündüz. 2020. Energy-Aware Analog Aggregation for Federated Learning with Redundant Data. In ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 1–7. (DOI: https://doi.org/10.1109/ICC40277.2020.9148853)