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# Demo: Energy-aware Dynamic Dimensioning of IoT Edge Clusters for Natural Environment Observation

Ammar Kazem, Guillaume Pierre, Laurent Longuevergne

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## To cite this version:

Ammar Kazem, Guillaume Pierre, Laurent Longuevergne. Demo: Energy-aware Dynamic Dimensioning of IoT Edge Clusters for Natural Environment Observation. IC2E 2025 - 13th IEEE International Conference on Cloud Engineering, IEEE, Sep 2025, Rennes, France. pp.1-3. [https://hal-05218930v1](https://hal.science/hal-05218930v1)

HAL Id: hal-05218930

[https://hal.science/hal-05218930v1](https://hal.science/hal-05218930v1)

Submitted on 22 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Demo: Energy-aware Dynamic Dimensioning of IoT Edge Clusters for Natural Environment Observation

Ammar Kazem

Univ Rennes, Inria, CNRS, IRISA

Rennes, France

ammar.kazem@irisa.fr

Guillaume Pierre

Univ Rennes, Inria, CNRS, IRISA

Rennes, France

guillaume.pierre@irisa.fr

Laurent Longuevergne

CNRS, Géosciences Rennes-UMR 6118

Rennes, France

laurent.longuevergne@univ-rennes.fr

**Abstract**—Edge computing is a vital component of the computing continuum, enabling near-real-time IoT data processing in remote settings. This technology is particularly useful for natural environment observation such as wildlife monitoring and flood detection where sensor devices may be located far from the urban centers. However, such systems face challenges arising from highly variable workloads and limited energy availability. Only 13% of French observatories are connected to a stable power grid, and others must instead rely on renewable sources like solar energy. This demonstration showcases a node consolidation algorithm that dynamically adjusts IoT edge cluster size based on real-time demand, significantly reducing energy consumption while maintaining acceptable quality of service.

**Index Terms**—IoT, edge computing, energy proportionality, environmental observatories, consolidation.

## I. INTRODUCTION

Edge technologies are an indispensable part of the computing continuum infrastructure which enables IoT data processing close to their sources while reducing processing latencies and pressure on critical communication infrastructure [1]. These technologies have been successfully utilized in application domains such as smart cities, transportation, health and industry 4.0 [2]. A new domain in which edge computing is being developed is the observation of the natural environment [3]. Environmental scientists define “environmental observatories” where they deploy sensors and actuators to support a wide range of research activities ranging from archiving historical data to detecting relevant events (floods, earthquakes etc.) and reacting to them in near real time.

Processing environmental data *in situ* faces significant challenges due to highly variable workloads ranging from diurnal CPU patterns to event-driven spikes. A majority of observatories have no access to regular power grids and rather rely entirely on renewable energy sources, requiring to limit the edge systems' energy usage [3].

We recently proposed a dynamic node consolidation algorithm to deliver sufficient quality of service according to real-time workload demands while reducing the cluster's power consumption by 33% compared to static approaches [4]. This demo aims to illustrate the efficiency of this algorithm and contrast it with static approaches.

## II. NODE CONSOLIDATION DESCRIPTION

An edge cluster for natural environment observation essentially works as a data stream processing system. Sensor data

are ingested continuously, and distributed to a series of applications in charge of processing them. When the processing capacity of the edge cluster is not sufficient to process all incoming data immediately after being received, unprocessed data are queued, and the overall system incurs a loss of quality of service as the generated outputs may be produced too late to be useful. The edge cluster must therefore have sufficient capacity to process incoming data with minimal delays, while avoiding overprovisioning which may result in an excessive energy usage.

Given an edge cluster, we define three strategies that implement radically different choices.

a) **Diamond Strategy:** this strategy aims to process all incoming data without delay, regardless of the energy spent. Therefore, it continuously keeps a sufficient number of compute modules powered on to support simultaneous activity peaks in all supported applications.

b) **Green Strategy:** this strategy aims to process all incoming data with minimal power usage, regardless of queuing delays before the data can be processed. Therefore, it continuously keeps just enough compute modules powered on to sustain the *mean* long-term expected workload. This guarantees that all incoming data will eventually be processed and that queues will not grow indefinitely, although there may be poor QoS. Conversely, it is highly energy efficient because it keeps a minimal number of nodes active with high utilization.

c) **Gold Strategy:** the goal of our proposed adaptive strategy is to approach the power efficiency of the Green strategy while providing a quality of service similar to that of the Diamond strategy [4]. It accomplishes this by dynamically switching compute modules on and off according to current demand and by constantly rebalancing the workload among active nodes using a node consolidator.

The node consolidation algorithm is designed as a MAPE (monitor, analyze, plan, and execute) loop. It queries Prometheus every minute to collect the CPU usage of the active nodes. The algorithm maintains a history of measurements over a fixed-size window, which it then analyzes to plan scaling decisions. These decisions are finally executed by switching nodes on or off.

During the analysis phase, the node consolidation algorithm categorizes individual nodes as underloaded, overloaded, or
<table><caption>TABLE I: Testbed Hardware Configuration.</caption><thead><tr><th>Name</th><th>Model</th><th>Specifications</th><th>K0s Role</th><th>OS</th></tr></thead><tbody><tr><td>RK1-0</td><td>RK1</td><td>4x ARM Cortex-A76<br>4x ARM Cortex-A55<br>LPDDR4 32 GB</td><td>Master<br>+ Worker</td><td>Ubuntu Server</td></tr><tr><td>RK1-1</td><td>RK1</td><td>4x ARM Cortex-A76<br>4x ARM Cortex-A55<br>LPDDR4 32 GB</td><td>Worker</td><td>Ubuntu Server</td></tr><tr><td>CM4-0</td><td>CM4</td><td>4x ARM Cortex-A72<br>LPDDR4 8 GB</td><td>Worker</td><td>Ubuntu Server</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 1: The hardware setup of the experiment that will be run during the demonstration.</figcaption></figure>

normal based on their CPU usage history and predefined thresholds. Then, it determines the state of the entire cluster as underloaded, overloaded, unbalanced, or normal based on the states of the nodes. An unbalanced cluster contains both overloaded and underloaded nodes. In the planning and execution phases, the algorithm adjusts the cluster. If the cluster is overloaded and there are additional available nodes, the algorithm scales up by turning on a new node. If the cluster is underloaded and can operate with fewer nodes, the algorithm scales down by turning off a node. For an unbalanced cluster, applications are redistributed to balance the load evenly. No action is taken if the cluster is normal.

### III. DEMONSTRATION PLAN

The demonstration hardware configuration consists of a Turing Pi 2.5 board [5] equipped with two RK1 [6] compute modules and one Raspberry Pi CM4 compute module. Table I provides details on their respective hardware configurations. The board has network capabilities, shared I/O, and shared power management. It can control the switching on and off of individual nodes through its Baseboard Management Controller. A Keysight 34465A digital multimeter will be connected to the Turing Pi to measure its power usage in real time. The multimeter will then be connected to a laptop via Ethernet to retrieve the data. CPU usage will be collected via Prometheus and sent to the laptop. Figure 1 illustrates the setup for the entire demonstration.

In the demonstration we will continuously execute the workload depicted in Figure 2 using the **Green strategy**. This workload, adapted from [4], was deliberately designed

<figure><img src="image_5.png" /><figcaption>Fig. 2: The aggregate workload scenario that will be run during the demonstration.</figcaption></figure>

with frequent peaks to highlight the strategy's behavior under varying loads. This workload requires an average processing power of 13,900 MIPS. Thus, the experiment will require a single RK1 compute module with 15,612 MIPS of processing capability under the Green strategy.

The demo with display the edge cluster's CPU utilization, data processing delay and power consumption in real time using reactive figures. Figures for the Gold and Diamond strategies from previous experiments will also be shown for comparison.

### IV. CONCLUSION

Edge computing clusters in remote environments, such as environmental observatories, face highly variable workloads and strong energy constraints. To address this, node consolidators can be used to dynamically adjust the cluster size by turning nodes on and off based on real-time demand. In this demonstration we will compare static edge clusters with dynamic edge clusters using node consolidators. This will highlight the ability node consolidation has in enhancing energy efficiency in edge clusters without compromising QoS.

### V. ACKNOWLEDGMENT

This research was supported by funding from the French government, administered by the National Agency for Research (ANR) through the Future Investment Program, integrated into France 2030, with grant reference ANR-21-ESRE-0014.

### REFERENCES

[1] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in *Proceedings of the first edition of the MCC workshop on Mobile cloud computing*, Aug. 2012. [Online]. Available: https://dl.acm.org/doi/10.1145/2342509.2342513

[2] A. Ahmed, H. Arkian, D. Battulga, A. J. Fahs, M. Farhadi, D. Giouroukis, A. Gougeon, F. O. Gutierrez, G. Pierre, P. R. Souza Jr, M. A. Tamiru, and L. Wu, "Fog computing applications: Taxonomy and requirements," Jul. 2019, arXiv:1907.11621 [cs].

[3] A. Kazem, G. Pierre, and L. Longuevergne, "Enhancing environmental observatories with fog computing," *Frontiers in Environmental Science*, vol. 13, Apr. 2025.
[4] A. Kazem, G. Pierre, and L. Longuevergne, "Energy-aware dynamic dimensioning of IoT edge clusters in remote environments," Submitted for publication, 2025.

[5] Turing Machines Inc, "Turing Pi 2, mini ITX cluster board," https://turingpi.com/product/turing-pi-2-5/.

[6] Turing Machines Inc, "Turing RK1," https://turingpi.com/product/turing-rk1/.