Jun 2026· IEEE Conference on Network Softwarization· pp. 523-528· 0 citations· 21 references
Computer Science
Abstract
The rapid development and scaling of mobile telecommunications networks, together with related domains such as the edge-cloud continuum have raised significant concerns regarding energy consumption and environmental sustainability. Addressing these concerns requires a focus on CPU energy consumption, as CPUs are among the largest energy consumers in these systems. This paper investigates existing techniques, with a focus on CPU idle states (C-states), performance states (P-states), and frequency scaling governors implemented at both hardware and software levels. These mechanisms enable the dynamic adjustment of CPU parameters, providing opportunities to optimize power consumption, frequency, voltage, and overall system performance. In this regard, three CPUs with different architectures from well-known manufacturers, Intel® and AMD®, are thoroughly examined. A comprehensive dataset, collected under three load scenarios (idle, medium, and high), is used to support the analysis, reflect realistic runtime conditions, and enable a comparison of the technological differences in how these parameters are exposed and utilized.
The new EMC+ proposal is an OS‐driven elasticity manager for container‐based environments that continuously estimates idle core cycles left by regular (inelastic) applications, and reallocates idle cores to elastic ones, even during short time intervals, and has minimal impact on the performance and QoS of colocated inelastic applications.
J. C. Saez, Carlos Bilbao, Manuel Prieto-Matías· Concurrency and Computation· 0 citations
The Function as a Service (FaaS) paradigm has emerged as a compelling architectural model for both cloud and edge computing environments, enabling the execution of self-contained functions triggered by specific events while abstracting from developers infrastructure management complexities such as load balancing and auto-scaling. In FaaS-enabled clusters, particularly within resource-constrained edge environments, precise resource consumption estimation becomes critical to optimize resource utilization, minimize latency, prevent system overloads, and ensure scalability. This paper addresses performance modeling challenges in FaaS-enabled distributed and decentralized edge computing systems, operating at the granularity level of both nodes and individual functions. We propose a Machine Learning-based framework designed to predict key performance indicators, including CPU utilization, memory, and energy consumption, based on incoming workload patterns, while simultaneously forecasting potential system overload conditions. Moreover, our approach introduces a profiling methodology that characterizes serverless functions according to their resource consumption profiles, thereby enabling accurate prediction of node-level resource demands without requiring detailed knowledge of individual deployed functions. Experimental validation demonstrates that our predictive models achieve 97% accuracy in anticipating node overload scenarios, providing a robust foundation for proactive resource management in edge-based FaaS deployments. Moreover, while our best individual, function-based regression models predict node-level CPU, RAM and power consumption with a Mean Absolute Percentage Error below 9% on average, our experiments highlight the effectiveness of function profiling and cluster-based modeling. On one hand a novel multi-target regressor based on a permutation-invariant neural architecture is proved to generalize effectively across previously unseen workload compositions in the tested scenarios, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document} scores ranging from 0.94 to 0.98, i.e., aligned with the performance of specialized single-target regression models. On the other hand, cluster-level models generalize effectively to previously unseen functions within the same usage class, maintaining prediction errors within practically acceptable ranges and often in the single-digit percentage range.
Federica Filippini, Marco Savi, Michele Ciavotta· Cluster Computing· 0 citations
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· Computers· 0 citations
The Cloud Continuum (CC) concept is to integrate private and public Far-Edge, Edge, and Central Cloud resources and provide uniform access to them for resource customers. The CC idea, by integrating all computing resources into a single domain, improves resource utilisation efficiency and simplifies the placement or migration of Virtual Functions (VFs). The paper focuses on the use of VFs' migration to optimise energy consumption in networks built atop CC. For this purpose, we adopt the CC-based 6G-Cloud project reference architecture, implemented using Kubernetes and cloud-native open-source tools (such as Karmada), as a platform for evaluating different algorithms aimed at optimising energy consumption. The work demonstrates key mechanisms for implementing the Energy Efficiency (EE) use case, showing benefits even with simple optimisation algorithms. Moreover, it highlights both the simplicity of the operational workflows and the resulting energy savings, while revealing important aspects of the EE problem in virtualised environments.
Slawomir Kuklinski, Robert Kołakowski, Bartlomiej Mastej· IEEE Conference on Network S...· 0 citations
The rapid growth of distributed computing paradigms such as cloud, edge computing, and large-scale data centers has significantly increased global energy consumption. As organizations increasingly rely on these systems for large-scale data processing, the need for energy-efficient methods has become critical due to rising operational costs and environmental concerns like carbon emissions. This paper analyzes energy-efficient data processing techniques in distributed environments, focusing on system-level optimization, algorithmic strategies, and resource management. It identifies major sources of energy consumption, including computation, data transfer, storage, and cooling, and highlights inefficiencies such as data redundancy, poor scheduling, network congestion, and underutilized resources. To address these challenges, the paper examines approaches such as energy-aware task scheduling, data locality optimization, dynamic voltage and frequency scaling (DVFS), virtualization, and workload consolidation. It also explores machine learning-based predictive models for adaptive resource allocation. A key contribution is the classification of these techniques across hardware, middleware, and application layers, along with a comparative analysis of their effectiveness. The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance. Overall, the study emphasizes the importance of coordinated, multi-layered strategies for achieving sustainable and energy-efficient distributed computing systems.
Seshagiri N· International Journal of Dat...· 0 citations