# Towards a Classification of Edge Service Orchestration Strategies Integrating Renewable Energy

Alexandre Sabbadin, Tom Guérout

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Alexandre Sabbadin, Tom Guérout. Towards a Classification of Edge Service Orchestration Strategies Integrating Renewable Energy. 22nd International Conference on Service-Oriented Computing (ICSOC 2024), Dec 2024, Tunis (Tunisie), Tunisia. hal-05010531

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# Towards a Classification of Edge Service Orchestration Strategies Integrating Renewable Energy

Alexandre Sabbadin¹[0009-0006-0068-1987]
supervised by Tom Guérout¹[0000-0002-6152-4065]

LAAS-CNRS, Université de Toulouse, Toulouse, France
{alexandre.sabbadin,tom.guerout}@laas.fr

**Abstract.** Edge computing, an emerging decentralized computing paradigm, extends Cloud infrastructure to the network edge to enable low-latency services and emphasizes widespread availability among users. Service orchestration plays a crucial role in optimizing performance and dynamically adapting to the changing context of Edge computing. Due to current environmental considerations and the large number of distributed servers in Edge computing, it is necessary to develop more sustainable and efficient methods for orchestration. To this end, renewable energy sources offer a promising approach to increase system sustainability. In this paper, we present a proposition for a comprehensive classification of edge service orchestration strategies that incorporate renewable energy sources in their infrastructures.

**Keywords:** Edge computing · Renewable energy · Classification · Energy efficiency · QoS · Service orchestration.

## 1 Motivations

In modern computing environments, we are moving towards increasingly distributed, flexible and reconfigurable architectures, both for the application structure and the infrastructure topology. This is exemplified by the paradigm of Edge computing [3], that involves deploying numerous devices across the architecture, relying on data generated by the lower layer of the IoT platforms. From an energy perspective, this represents a significant shift from architectures studied a decade ago, such as Cloud Computing, as resources now need to be powered independently and are no longer dependent on a single data-center. Consequently, the challenges of energy efficiency evolve according to this architectural shift [5]. Furthermore, a new approach to power these systems must be closely examined. It is no longer about minimizing the consumption of a large number of connected resources, but rather about efficiently utilizing the available energy for each device (or a set of devices). Indeed, current storage systems, mainly different types of batteries available on the market, need to be optimally managed over a finite time horizon and capacity degradation over charge/discharge
cycles must be considered in order to fully utilize a limited amount of energy. A new vision of energy management in the coming years will likely need to rely on better estimation or prediction of the usage of different zones in terms of service deployment within the architecture, for example, to move services based on the available energy in each zone and to make use of devices powered by green energy. Although the efficiency and the ratio of manufacturing cost over energy produced are still frequently questioned, these green energy production solutions remain the only decarbonized options available to us. Again, their use must be “controlled”, “minimized”, or “optimized” because their production levels will always remain uncertain. The complexity of these energy-related challenges inevitably impacts the management of high-level quality-of-service (QoS). To effectively address these issues, advanced modeling of future systems is necessary. This modeling should incorporate both QoS metrics (e.g. time horizons, availability, platform dynamism, etc.) and energy efficiency considerations. Integrating these aspects will help clarifying the constraints and objectives related to service availability and energy consumption.

In modern infrastructures, virtualized services, through Software-Defined Networking (SDN) and Network Function Virtualization (NFV), replace the need for dedicated hardware, enabling more flexible and scalable service deployment. Novel holistic approaches to energy management and service orchestration mark a significant progression from previous generations, supporting the global shift towards more sustainable and efficient technological ecosystems. Therefore, service orchestration plays a key role in addressing these challenges by automating the life cycle management of complex services [6]. Agile service orchestration enhances flexibility and responsiveness, accommodating both vertical and horizontal dimensions. Vertically, it involves the virtualization of services from the network to the application layers, improving resource utilization and scalability. Horizontally, it extends across the Cloud Continuum, integrating services from centralized data-centers to Edge and Fog computing environments. Such an approach enables the dynamic resource allocation, hence meeting the diverse needs of modern applications and users. Moreover, a multi-objective approach to service orchestration could optimize not only resource usage and scalability, but also balance conflicting objectives such as energy efficiency, service latency and other QoS metrics. To increase the system sustainability, renewable energy sources is a promising approach that needs to be explored. In this paper, we describe our proposition for a classification of edge service orchestration strategies that implement renewable energy sources in their infrastructures. The rest of the paper is organized as follows. Section 2 describes related work. Section 3 introduces our proposed research questions and discuss our research plan for the PhD. Finally, Section 4 concludes our work.

## 2 Related work

Edge and Fog computing are often used interchangeably to bring Cloud capabilities to the edge of the network. However, [8] considers that Edge computing is
co-located with end devices while Fog computing might be further away. Also, Edge computing focus more on things-level operations while Fog computing focus on infrastructure. A number of works have addressed renewable energies and service orchestration but often treat them as separate issues. The authors of [11] discuss Fog computing within the context of smart cities, presenting use cases and features without tackling the intersection of renewable energies and service orchestration. Similarly, [9] focus primarily on existing Edge computing systems and offers a comparison of open-source tools, mentioning energy efficiency in systems but not in orchestration schemes. Several studies have investigated renewable energy solutions specifically for IoT or small devices using energy harvesting. In [10], energy consumption activities in edge AI are categorized, considering renewable energies only in the context of energy harvesting for small devices. The authors of [2] concentrate on energy harvesting mainly for IoT, dividing the discussion into two categories: energy harvesting and energy saving, with Fog computing considered only in the latter. These works demonstrate the limited scope of renewable energy considerations, typically confined to IoT applications rather than edge infrastructure or service provider scenarios. Some surveys provide a narrow focus on certain aspects of renewable energy usage. The authors of [7] consider solar power for roadside units and discuss energy efficiency from a technological aspect, focusing more on Unmanned Aerial Vehicles (UAVs) than on green energy sources. Moreover, strategies that apply to UAV-based infrastructures might not be applicable to traditional edge infrastructures. The authors of [13] discuss an energy-harvesting mobile Edge computing (EH-MEC) system, yet most cited papers do not implement this system and renewable energy resources are primarily used for resource allocation rather than provisioning. These works highlight the restricted exploration of renewable energies in the existing literature, often focusing on specific use cases or technologies without a comprehensive approach to their integration in service orchestration.

We want to address the existing gaps in the literature by focusing on the intersection of service orchestration at the edge with the implementation of renewable energy sources. Unlike previous studies that primarily consider renewable energies through energy harvesting for IoT devices, our work would aim to adopt a broader perspective, examining the benefits of renewable energy sources for potential infrastructure or service providers. Additionally, the integration of renewable energy sources in Edge computing alters the approach to defining multi-objective optimization problems for energy-efficient service orchestration. Unlike traditional scenarios that primarily balance energy consumption and performance, the inclusion of renewable energy resources introduces additional complexity. Optimization models must now account for the intermittent nature of renewable sources, varying levels of "greenness" in energy mix and constraints of limited available energy and battery storage. These new models should be identified. By analyzing levers to improve the energy efficiency of edge systems, we want to provide a comprehensive overview of the pertinent considerations for implementing renewable energies in service orchestration.
# 3 Ongoing contributions

## 3.1 Research questions

The most frequently used three-layer model has been adopted, encompassing Cloud, Fog and IoT layers, with Edge representing a specific intermediate infrastructure within the Fog layer, closer to users. The Fog layer is geographically distributed between the Cloud and IoT layers. Fog nodes are heterogeneous in terms of resources and energy, and make their resources available through virtualization technologies. With this model, the use of a graph architecture has been considered, as demonstrated in Figure 1, with Fog nodes as vertices and their connections (wired or wireless) as edges. Services are defined as microservices (docker type) and can be intended for the user or as support for network operation. Our objective is to study / produce orchestration methods for these services, taking into account the heterogeneous energy aspect of Fog architectures, while maintaining the best possible QoS level.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Service-oriented Edge computing model</figcaption></figure>

We want to explore this energy aspect in a systematic literature review, following the guidelines of Petersen et al. [12]. In this literature review, we would aim to present traditional service orchestration strategies that enhance energy efficiency in edge environments. These strategies should not only optimize resource utilization but also meet sustainability goals. The environmental impact of energy consumption in Edge computing heavily depends on energy-efficient service orchestration, especially when compared to Cloud computing. With a first research question RQ1, we seek to identify orchestration strategies that can improve energy efficiency.

Additionally, this literature review would aim to identify new opportunities introduced by the integration of renewable energy in edge service orchestration. The trade-off is no longer solely between energy consumption and performance but now includes new optimization objectives and constraints related to the in-
termittency and limited availability of green energy. To address this, we propose
a second research question RQ2 focusing on this challenge.

- RQ1) What orchestration levers are traditionally used to enhance energy efficiency in Edge computing?

- RQ2) What new levers does renewable energy introduce for service orchestration in Edge computing?

**3.2 Research plan**

In this section we describe the current status of the doctoral thesis one year after
its start in September 2023.

The initial phase has involved a comprehensive review of the state-of-the-art
in energy-efficient Edge computing, QoS parameters and service orchestration
techniques, that should result in the submission of a survey paper with the classi-
fication integrating renewable energy. In parallel, we work on the development of
an architecture model encompassing the Edge infrastructure, energy consump-
tion models and relevant QoS metrics. The core of the research will involve
formulating a multi-criteria optimization problem that addresses the trade-offs
between energy efficiency and QoS in Edge computing scenarios.

To tackle this complex problem, the research will proceed with a selection of
appropriate optimization methods and algorithms. We plan to use Deep Rein-
forcement Learning (DRL) methods, as these techniques allow for generalizing
dynamic infrastructures. Concurrently, a suitable simulation platform as Sim-
Grid [4] will be chosen to model and evaluate the proposed solutions accurately,
while real implementations are planned for the experimental platform Grid'5000
[1]. The final phase of the research plan involves defining a series of experiments
to validate the effectiveness of the proposed approach. These experiments will be
designed to test various scenarios and configurations, providing a comprehensive
assessment of the optimization strategies in Edge computing environments.

4 Conclusion

In conclusion, this paper presents the outcomes of the first year of a three-year
PhD study focusing on Edge computing, with emphasis on service orchestration
and energy efficiency. We have conducted a comprehensive state-of-the-art re-
view, which has led to the development of a novel classification system for edge
service orchestration strategies integrating renewable energy. This work lays a
foundation for the remaining two years of research, with several challenging fu-
ture directions identified. This includes further exploration of sustainable energy
solutions in Edge computing environments and optimization of service orches-
tration techniques, which includes implementations of renewable energy and the
practical study of its associated reliability and variability.

**Acknowledgments.** This work was supported by the ANR under the France 2030 program, grant "NF-NAI: ANR-22-PEFT-0003".
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