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
This paper proposes 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, and introduces a profiling methodology that characterizes serverless functions according to their resource consumption profiles.
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 proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
Seshagiri N· International Journal of Dat...· 0 citations
