# Event-Driven Adaptation in the Computing Continuum
## Using Software Variability

Martin Molli, Daniel Balouek, Paul Temple, Thomas Ledoux

► To cite this version:

Martin Molli, Daniel Balouek, Paul Temple, Thomas Ledoux. Event-Driven Adaptation in the Computing Continuum Using Software Variability. UCC 2025 - IEEE/ACM 18th International Conference on Utility and Cloud Computing, Dec 2025, Nantes, France. pp.1-2, 10.1145/3773274.3774672. hal-05379151

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Submitted on 24 Nov 2025

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# Event-Driven Adaptation in the Computing Continuum Using Software Variability

Martin Molli

IMT Atlantique, Nantes Université, École Centrale Nantes
CNRS, Inria, LS2N, UMR 6004, F-44000
Nantes, France

Paul Temple

University of Rennes and IRISA
Rennes, France

## Abstract

The computing continuum aggregates edge, fog, and cloud infrastructure layers, promising lower latencies and improved performance for data-driven applications. In this context, keeping service-level objectives (SLOs) is challenging due to fluctuations in resource capacities and network uncertainties.

We present an adaptive runtime that manages software configuration parameters, application placement, and scaling at runtime to enforce SLOs. Our approach aims to maintain a separation between developers and providers roles while providing performance estimations for previously unseen configurations. This paper describes the proposed architecture, event model, and preliminary results over the Grid'5000 testbed.

## Keywords

Computing Continuum, Edge Computing, Cloud Computing, Automatic Computing, Software Variability

## ACM Reference Format:

Martin Molli, Daniel Balouek, Paul Temple, and Thomas Ledoux. 2025. Event-Driven Adaptation in the Computing Continuum Using Software Variability. In *2025 IEEE/ACM 18th International Conference on Utility and Cloud Computing (UCC '25), December 1-4, 2025, France, France*. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3773274.3774672

## 1 Introduction

The Computing Continuum unifies edge, fog, and cloud into a single infrastructure that promises to improve latency-sensitive pipelines [2, 4]. Continuum applications are often built by decomposing them into independent functions, following the Function-as-a-Service (FaaS) model [8], in which platforms enforce a clear division between developers and providers [6].

Function placement typically targets latency and bandwidth through optimization or learning-based techniques [7, 9], while runtime adaptation is commonly organized through MAPE-K loops.

Daniel Balouek

IMT Atlantique, Nantes Université, École Centrale Nantes
CNRS, Inria, LS2N, UMR 6004, F-44000
Nantes, France

Thomas Ledoux

IMT Atlantique, Nantes Université, École Centrale Nantes
CNRS, Inria, LS2N, UMR 6004, F-44000
Nantes, France

MAPE-K-based approaches can benefit from an explicit definition of software configuration parameters across actors, enabling reconfiguration at runtime. Feature models from Software Product Line engineering [3, 5] provide such explicit definitions. We propose a system that exploits software configuration parameters to enforce SLOs and dynamically adapt the application and infrastructure.

## 2 System Model and Assumptions

**Applications.** We consider an application as a distributed dataflow: a FIFO DAG of microservices sending messages over HTTP. We treat each microservice as a black box.

We distinguish two principal roles in the continuum. Application owners (developers/operators) seek cost-efficient deployments that meet service-level objectives, such as thresholds on key metrics. Resource providers (edge and cloud operators) expose infrastructures from single-board devices with a few GiB of RAM at the edge to high-performance nodes in the cloud.

**Actors.** *Developers* express possible configuration parameters using a feature model. They are represented as hierarchical models that different aspects of a software product [3]. We define feature resource needs, metric thresholds, and a multi-objective goal based on throughput, latency, or cost. *Providers* share machine capacities and network topologies as a graph with latency/bandwidth on edges and machine capacity on nodes.

**Observability.** Both actors use centralized monitoring to observe their point of view. We sample throughput, mean latency, CPU, and RAM every 5 seconds in 10-minute tumbling windows.

## 3 Architecture and Adaptation Workflow

Our runtime has five parts:

(1) **Benchmarking Engine:** runs selected configurations on provider machines to build a performance dataset.

(2) **Performance Estimator:** Train a model (e.g., decision trees) to interpolate the performance of untested configurations.

(3) **Optimization Solver:** searches the configuration under resource and topology limits to find options that meet SLO and optimize the specified goals.

(4) **Event Handler:** listens to developer, provider, and system events. SLO deviations have the highest priority.

(5) **Orchestrator:** applies changes (software settings, placement, scaling) via container orchestration.

<footnote>Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).</footnote>

<footnote>UCC '25, France, France</footnote>

<footnote>© 2025 Copyright held by the owner/author(s).</footnote>

<footnote>ACM ISBN 979-8-4007-2285-1/2025/12</footnote>

<footnote>https://doi.org/10.1145/3773274.3774672</footnote>
We design a runtime based on previous work using the MAPE-K paradigm [10], which integrates the runtime parts.

(1) Monitor: Collect monitoring metrics

(2) Analyze: Trigger events on deviations and context changes.

(3) Plan Performance prediction and multi-objective selection.

(4) Execute Redeployment of the application

## 4 Event Model

Events originate from both the provider and the developer and are emitted when the monitored system exhibits changes within the defined sliding window. Table 1 summarizes the events and triggers.

<table><caption>Table 1: Event sources and triggers</caption><thead><tr><td>Source</td><td>Event</td><td>Trigger</td></tr></thead><tbody><tr><td>Provider</td><td>CapacityChange</td><td>CPU or RAM changes by ≥ 20%</td></tr><tr><td>Provider</td><td>NetworkUpdate</td><td>Latency or bandwidth changes by ≥ 20%</td></tr><tr><td>Provider</td><td>NodeChange</td><td>A node joins or leaves</td></tr><tr><td>Developer</td><td>RequirementsUpdate</td><td>SLOs or features change</td></tr><tr><td>Developer</td><td>PerformanceDeviation</td><td>Metrics deviate by ≥ 10% from SLO</td></tr></tbody></table>

We quantify performance variability in the video-processing workflow. As shown in Figure 1, modifying only the resolution results in non-linear shifts in throughput and latency, demonstrating significant software variability that can be leveraged for adaptation.

## 5 Evaluation

<figure><img src="image_2.png" /><figcaption>Figure 1: Throughput and makespan for different resolution configurations. Each point represents the performance for a given resolution, while the red star with error bars indicates the reference configuration (mean ± SD, n = 4)</figcaption></figure>

**Assumptions:** A₁: Under capacity changes Δ ∈ {10, ..., 100}%, the runtime keeps SLOs. A₂: The runtime stays stable when SLO thresholds vary by ±10~30%. A₃: Adaptation time remain tractable up to 10⁵ nodes.

**Evaluation setup:** We use the Grid'5000 testbed [1] with a video processing use case as the application. We use K3s as an orchestrator, and OpenTelemetry/Prometheus for monitoring.

**Metrics.** SLO satisfaction rate, decision latency, deployment overhead, benchmarking CPU-hours, prediction error.

## 6 Conclusion

We have presented an adaptive runtime that integrates software variability, developer, and provider context to maintain SLOs across the computing continuum. Our approach features benchmarking with performance interpolation and multi-objective optimization to select feasible configurations. Future work includes evaluating the effectiveness of adaptation and prediction accuracy.

## Acknowledgments

This work was supported by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program, reference "ANR-23-PECL-0008".

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