Adnei Willian Donatti

arXiv2026

The Impact of Process Competition on Energy Consumption: Analysis and Modeling

With the development of distributed systems, the need to manage the sharing of machines among multiple simultaneous users arises. In the cloud computing context, the instantiation of virtual machines and containers by different users utilizing the same infrastructure leads to a dispute for physical computational resources. In this regard, this paper analyses a process's energy consumption as a function of the competition for computational resources it encounters. Investigating this behavior is fundamental for many applications, such as pricing in cloud computing services, and for task scheduling and load balancing, while increasing energy efficiency. To determine this behavior, experiments were conducted and resulted in a dependency on the number of processor cores of the physical machine hosting the process. As the number of cores increases, the process's energy consumption as a function of the competition it faces transitions from linear to a root function.

cs.DC↗

arXiv2025

Energy Efficiency in Network Slicing: Survey and Taxonomy

Network Slicing (NS) is a fundamental feature of 5G, 6G, and future mobile networks, enabling logically isolated virtual networks over shared infrastructure. As data demand increases and services diversify, ensuring Energy Efficiency (EE) in NS is vital (not only for operational cost savings but also to reduce the Information and Communication Technology (ICT) sector's environmental footprint). This survey addresses the need for a comprehensive and holistic perspective on energy-efficient NS by reviewing and classifying recent strategies across the NS life cycle. Our contributions are threefold: (i) a thorough review of state-of-the-art techniques aimed at reducing energy consumption in NS; (ii) a novel taxonomy that organizes strategies into infrastructure, path/route, and slice operation levels; and (iii) the identification of open challenges and research directions, with a focus on systemic, cross-layer, and AI-driven approaches. By consolidating insights from recent developments, our work bridges existing gaps in the literature, offering a structured foundation for researchers and practitioners to design, evaluate, and improve energy-efficient network slicing systems.

cs.NI↗