arXiv subjects

Klervie Toczé

Publications and source records attributed to Klervie Toczé.

5 recordsLinked to original sources

arXiv2026

Toward an Architectural Blueprint to Observe Sustainability in and by Software Systems

Enabling observability in software systems brings many benefits. It can, for example, ease the identification of issues or the implementation of improvements. It is especially critical to be able to observe sustainability-related dimensions of systems to know and mitigate their impact. However, adding observability to a system, especially related to software sustainability, requires technical knowledge that may not be available in every project that would benefit from it. In this work, we propose an architectural blueprint along with its deployment code that can be used to facilitate the addition of observability in software systems. As a special case, it includes measuring the energy consumption of software. This toolkit provides support in defining which components are necessary for a given use case and for structuring their deployment. Moreover, we exemplify the addition of observability in two different use cases.

cs.SE↗

arXiv2025

Supporting a Sustainable and Inclusive Urban Agriculture Federation using Dashboarding

Reliable access to food is a basic requirement in any sustainable society. However, achieving food security for all is still a challenge, especially for poor populations in urban environments. The project Feed4Food aims to use a federation of Living Labs of urban agriculture in different countries as a way to increase urban food security for vulnerable populations. Since different Living Labs have different characteristics and ways of working, the vision is that the knowledge obtained in individual Living Labs can be leveraged at the federation level through federated learning. With this specific goal in mind, a dashboarding tool is being established. In this work, we present a reusable process for establishing a dashboard that supports local awareness and decision making, as well as federated learning. The focus is on the first steps of this creation, i.e., defining what data to collect (through the creation of Key Performance Indicators) and how to visualize it. We exemplify the proposed process with the Feed4Food project and report on our insights so far.

cs.CY↗

arXiv2025

Energy Metrics for Edge Microservice Request Placement Strategies

Microservices are a way of splitting the logic of an application into small blocks that can be run on different computing units and used by other applications. It has been successful for cloud applications and is now increasingly used for edge applications. This new architecture brings many benefits but it makes deciding where a given service request should be executed (i.e. its placement) more complex as every small block needed for the request has to be placed. In this paper, we investigate energy-centric request placement for services that use the microservice architecture, and specifically whether using different energy metrics for optimization leads to different placement strategies. We consider the problem as an instance of a traveling purchaser problem and propose an integer linear programming formulation. This formulation aims at minimizing energy consumption while respecting latency requirements. We consider two different energy consumption metrics, namely overall or marginal energy, when applied as a measure to determine a placement. Our simulations show that using different energy metrics indeed results in different request placements. The paper presents several parameters influencing the extent of this difference.

cs.DC↗

arXiv2024

Energy-aware Distributed Microservice Request Placement at the Edge

Microservice is a way of splitting the logic of an application into small blocks that can be run on different computing units and used by other applications. It has been successful for cloud applications and is now increasingly used for edge applications. This new architecture brings many benefits but it makes deciding where a given service request should be executed (i.e. its placement) more complex as every small block needed for the request has to be placed. In this paper, we investigate decentralized request placement (DRP) for services using the microservice architecture. We consider the DRP problem as an instance of a traveling purchaser problem and propose an integer linear programming formulation. This formulation aims at minimizing energy consumption while respecting latency requirements. We consider two different energy consumption metrics, namely overall or marginal energy, to study how optimizing towards these impacts the request placement decision. Our simulations show that the request placement decision can indeed be influenced by the energy metric chosen, leading to different energy reduction strategies.

cs.DC↗

arXiv2018

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

cs.DC↗