LEAF: Simulating Large Energy-Aware Fog Computing Environments

TL;DR

LEAF is a comprehensive simulation tool for modeling energy consumption in large-scale fog computing environments, aiding research on energy-efficient architectures and strategies.

Contribution

It introduces a holistic, granular energy consumption model combining analytical and discrete-event modeling for fog computing simulations.

Findings

LEAF can simulate thousands of devices with complex applications.

It effectively evaluates energy-saving strategies in fog environments.

Demonstrated in a smart city traffic scenario.

Abstract

Despite constant improvements in efficiency, today's data centers and networks consume enormous amounts of energy and this demand is expected to rise even further. An important research question is whether and how fog computing can curb this trend. As real-life deployments of fog infrastructure are still rare, a significant part of research relies on simulations. However, existing power models usually only target particular components such as compute nodes or battery-constrained edge devices. Combining analytical and discrete-event modeling, we develop a holistic but granular energy consumption model that can determine the power usage of compute nodes as well as network traffic and applications over time. Simulations can incorporate thousands of devices that execute complex application graphs on a distributed, heterogeneous, and resource-constrained infrastructure. We evaluated our publicly available prototype LEAF within a smart city traffic scenario, demonstrating that it enables research on energy-conserving fog computing architectures and can be used to assess dynamic task placement strategies and other energy-saving mechanisms.