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Unmanned aerial vehicle-enabled mobile edge computing for semantic communications 支持无人机的语义通信移动边缘计算

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-05 DOI: 10.1016/j.comcom.2026.108411

Liyuan Xie , Wancheng Xie , Huabing Lu , Helin Yang

With the rapid evolution of wireless networks and the increasing demand for flexible communication and computing services, unmanned aerial vehicles (UAVs) have emerged as a promising solution to enhance the performance of these networks. This paper investigates a UAV-assisted mobile edge computing (MEC) system with semantic communication (SemCom) to improve the efficiency of wireless networks by transmitting only meaningful information, thereby reducing bandwidth and computational resource requirements. We propose a resource scheduling approach to minimize the weighted sum of overall latency for task processing and energy consumption under malicious jamming attacks. The approach jointly optimizes device scheduling, UAV trajectory, task offloading ratio, bandwidth allocation, and the number of transmitted SemCom symbols under different constraints. The optimization problem is complex and non-convex, involving ongoing decision-making due to constantly changing parameters. To address this challenge, we present a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm for real-time resource management. The proposed PPO-based resource scheduling approach effectively schedules both communication and computing resources to minimize the cost of the UAV-enabled wireless network against jamming attacks. Simulation-based performance analysis indicates that the PPO-based SemCom scheme reduces task execution latency and energy consumption compared to baseline approaches across various network scenarios. The proposed framework provides valuable insights into the design and optimization of UAV-assisted MEC systems with SemCom for enhanced wireless network performance in the presence of adversarial jamming.

随着无线网络的快速发展以及对灵活通信和计算服务的需求不断增加，无人机（uav）已经成为提高这些网络性能的一种有前途的解决方案。本文研究了一种具有语义通信（SemCom）的无人机辅助移动边缘计算（MEC）系统，通过仅传输有意义的信息来提高无线网络的效率，从而减少带宽和计算资源需求。我们提出了一种资源调度方法，以最小化在恶意干扰攻击下任务处理的总延迟和能量消耗的加权总和。该方法对不同约束条件下的设备调度、无人机轨迹、任务卸载比、带宽分配和发送SemCom符号数进行了联合优化。优化问题是一个复杂的非凸问题，涉及由于参数不断变化而导致的持续决策。为了解决这一挑战，我们提出了一种基于近端策略优化（PPO）的深度强化学习（DRL）算法，用于实时资源管理。所提出的基于ppo的资源调度方法有效地调度通信和计算资源，使无人机无线网络抵御干扰攻击的成本最小化。基于仿真的性能分析表明，与各种网络场景的基线方法相比，基于ppo的SemCom方案减少了任务执行延迟和能耗。所提出的框架为无人机辅助MEC系统的设计和优化提供了有价值的见解，该系统具有SemCom，可以在对抗性干扰存在的情况下增强无线网络性能。

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引用次数: 0

Dynamic resource allocation for digital twin-enhanced hierarchical federated learning in sustainable internet of things 可持续物联网中数字孪生增强分层联邦学习的动态资源分配

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-03 DOI: 10.1016/j.comcom.2025.108410

Ze Wei , Rongxi He , Xiaojing Chen , Chengzhi Song

This paper proposes a digital twin (DT)-enhanced hierarchical federated learning framework for sustainable Internet of Things (IoT) networks. In this framework, mobile edge computing servers coordinate collaborative training, while DTs maintain real-time physical-virtual synchronization. Our core contributions are threefold. First, to tackle device heterogeneity, we propose two mechanisms: (1) an elastic time window that dynamically adapts aggregation deadlines based on median training times while incorporating distance-aware resource compensation to mitigate channel degradation, and (2) a DT-enhanced weighting strategy that dynamically balances energy sustainability, channel quality, and model freshness while guaranteeing convergence through closed-loop cross-layer coordination. Second, we derive a convergence bound explicitly linked to the device participation ratio, establishing a direct theoretical connection between resource allocation and learning performance. Then, through theoretical analysis, it can be found that reducing training latency and energy consumption by jointly optimizing computing and communication resources, as well as EH duration, is key to maximizing this ratio without compromising the reliability of the gradients, thereby indirectly enhancing convergence. Third, guided by this insight, we formulate a mixed-integer nonlinear programming problem that aims to maximize the participation ratio while jointly minimizing energy consumption and training latency, by optimizing the energy harvesting time, collaboration ratio, and communication/computation resources. To solve this NP-hard problem, we propose a DT-driven decomposition framework that partitions it into two subproblems, which are then solved by three DT-driven algorithms with provable near-optimality guarantees. Experimental results validate the superiority of our approach, demonstrating significant improvements in convergence performance, latency, energy efficiency, and participant sample rate, while also advancing the sustainability of FL.

本文提出了一种用于可持续物联网（IoT）网络的数字孪生（DT）增强分层联邦学习框架。在这个框架中，移动边缘计算服务器协调协同训练，而dt保持实时物理-虚拟同步。我们的核心贡献有三个方面。首先，为了解决设备异构问题，我们提出了两种机制：(1)弹性时间窗，该弹性时间窗基于中值训练时间动态适应聚合截止日期，同时结合距离感知资源补偿以减轻信道退化；(2)dt增强加权策略，该策略动态平衡能量可持续性、信道质量和模型新鲜度，同时通过闭环跨层协调保证收敛。其次，我们推导了一个明确与设备参与率相关的收敛界，建立了资源分配与学习绩效之间的直接理论联系。然后，通过理论分析可以发现，在不影响梯度可靠性的前提下，通过联合优化计算资源和通信资源来减少训练延迟和能量消耗，以及EH持续时间，是最大化该比值的关键，从而间接增强收敛性。第三，在此基础上，通过优化能量收集时间、协作比例和通信/计算资源，提出了一个混合整数非线性规划问题，以最大限度地提高参与率，同时最大限度地降低能耗和训练延迟。为了解决这个np困难问题，我们提出了一个dt驱动的分解框架，该框架将其划分为两个子问题，然后通过三个具有可证明的近最优性保证的dt驱动算法来解决这两个子问题。实验结果验证了我们的方法的优越性，证明了在收敛性能、延迟、能源效率和参与者采样率方面的显著改进，同时也提高了FL的可持续性。

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引用次数: 0

CISF: Consensus-based Information Sharing Framework for robust consistency in UAVs swarm disaster response 基于共识的无人机群灾响应鲁棒一致性信息共享框架

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-02 DOI: 10.1016/j.comcom.2025.108408

Xuefeng Du , Yanqi Cheng , Li Yin , Ning Tong , Fengqiang Xu , Fengqi Li

In disaster response scenarios, distributed unmanned aerial vehicle (UAV) swarms face substantial challenges in maintaining real-time information consistency due to network instability, communication delays, and potential Byzantine faults. Traditional approaches often fail to balance fault tolerance, communication latency, and task execution efficiency under such dynamic and adversarial conditions. This paper proposes the Consensus-based Information Sharing Framework (CISF), a novel solution specifically designed to ensure information consistency in dynamic disaster environments. CISF integrates a Stratified Parallel Byzantine Fault Tolerance (SPBFT) mechanism — optimized via a dynamic capability-reputation evaluation model — with a Multi-Round Search and Patrol Model (MSPM) based on an improved Cuckoo Search algorithm. MSPM employs a multi-objective fitness function to jointly optimize temporal efficiency, spatial coverage, and task priority, enabling comprehensive area exploration and continuous information validation. Theoretical analysis derives the optimal hierarchical ratio and the maximum fault tolerance threshold for CISF. Simulation results show that CISF maintains 93.8% consistency under Byzantine interference and reduces consensus latency by up to 56.2%, while remaining effective in highly dynamic environments. Overall, this study establishes a robust and efficient framework for achieving real-time, fault-tolerant information consistency in interference-prone UAV networks, offering broad applicability for future swarm-based disaster response systems.

在灾难响应场景中，由于网络不稳定、通信延迟和潜在的拜占庭故障，分布式无人机（UAV）群在保持实时信息一致性方面面临着巨大的挑战。在这种动态和对抗的条件下，传统的方法往往无法平衡容错性、通信延迟和任务执行效率。本文提出了基于共识的信息共享框架（CISF），这是一种专门用于确保动态灾难环境下信息一致性的新解决方案。CISF集成了分层并行拜占庭容错（SPBFT）机制（通过动态能力-声誉评估模型优化）和基于改进布谷鸟搜索算法的多轮搜索和巡逻模型（MSPM）。MSPM采用多目标适应度函数，共同优化时间效率、空间覆盖和任务优先级，实现区域的综合勘探和信息的持续验证。理论分析得出了CISF的最优层次比和最大容错阈值。仿真结果表明，CISF算法在拜占庭干扰下保持了93.8%的一致性，减少了56.2%的共识延迟，同时在高动态环境下仍然有效。总体而言，本研究建立了一个鲁棒且高效的框架，用于在易受干扰的无人机网络中实现实时、容错的信息一致性，为未来基于群体的灾害响应系统提供了广泛的适用性。

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引用次数: 0

Joint design of resource allocation and QoS enhancement via serial optimization in UAV-NOMA communications 基于串行优化的无人机- noma通信资源分配与QoS增强联合设计

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-30 DOI: 10.1016/j.comcom.2025.108409

Zhongyu Wang , Yanan Lian , Jie Zeng , Zheng Chang , Tiejun Lv

We investigate the challenges of user pairing, power allocation, and bandwidth allocation problems in unmanned aerial vehicle (UAV) systems that employ nonorthogonal multiple access (NOMA) for communication with multiple ground users. The primary objective is to maximize the system’s achievable transmission rate while ensuring the users’ quality of service (QoS) requirements under a constrained total power budget. Considering the nonconvexity of the original problem and the interdependencies among multiple optimization variables, the problem is decomposed into three subproblems to optimize power and bandwidth allocation. To increase resource utilization and address user pairing challenges, a serial-optimized communication scheme is proposed, which leverages an optimized block coordinate descent (OP-BCD) method to sequentially solve the subproblems. Specifically, the power allocation strategy is optimized using an optimized deep Q-network (DQN) combined with a gradient ascent approach, whereas the intergroup bandwidth is optimized via a sequential least squares programming (SLSQP). Simulation results demonstrate that the proposed group matching method significantly enhances resource utilization and fairness compared to other user pairing strategies. Moreover, the proposed scheme effectively increases the system transmission rate and resource efficiency.

本文研究了采用非正交多址（NOMA）与多个地面用户通信的无人机系统中的用户配对、功率分配和带宽分配问题。主要目标是在有限的总功率预算下，在保证用户服务质量（QoS）需求的同时，最大限度地提高系统可实现的传输速率。考虑到原问题的非凸性和多个优化变量之间的相互依赖性，将问题分解为三个子问题，对功率和带宽分配进行优化。为了提高资源利用率和解决用户配对难题，提出了一种串行优化通信方案，该方案利用优化块坐标下降（OP-BCD）方法对子问题进行顺序求解。具体而言，采用优化的深度q网络（DQN）和梯度上升方法对功率分配策略进行优化，而通过顺序最小二乘规划（SLSQP）对群间带宽进行优化。仿真结果表明，与其他用户配对策略相比，所提出的分组匹配方法显著提高了资源利用率和公平性。此外，该方案有效地提高了系统传输速率和资源效率。

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引用次数: 0

Making cellular networks crisis-proof: Towards island-ready, resilient-by-design 6G communication networks 使蜂窝网络防危机：迈向孤岛就绪、设计弹性的6G通信网络

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-30 DOI: 10.1016/j.comcom.2025.108407

Leon Janzen, Matthias Hollick

5G and 5G-Advanced cellular networks are vulnerable to local outages resulting from disasters or targeted attacks. This fragility stems from the reliance on the central core network involved for most 5G connectivity use cases. Crisis-struck areas isolated from the cellular core network form islands, where crisis response is hindered by the unavailability of recovery-relevant services, such as emergency calls, cell broadcasts, messengers, and news apps. Our concept of island-ready, resilient-by-design 6G communication networks envisions local cellular connectivity allowing users to connect to local application servers, which is currently impossible. In our conceptualization, we follow an all-society approach, as realizing island connectivity requires the cooperation of multiple actors, including users, operators, developers, providers, and authorities. We evaluate the island readiness of 5G and 5G-Advanced systems and outline the open challenges stakeholders must address for full island readiness, such as decentralizing the 6G core network and designing local-first application architectures.

5G和5G- advanced蜂窝网络很容易受到灾难或有针对性攻击造成的局部中断的影响。这种脆弱性源于对大多数5G连接用例所涉及的中央核心网络的依赖。与蜂窝核心网络隔离的受危机影响地区形成孤岛，由于无法获得与恢复相关的服务，如紧急呼叫、蜂窝广播、信使和新闻应用程序，危机应对受到阻碍。我们的概念是孤岛就绪，设计灵活的6G通信网络，设想本地蜂窝连接允许用户连接到本地应用服务器，这是目前不可能的。在我们的概念中，我们遵循全社会的方法，因为实现岛屿连接需要多个参与者的合作，包括用户、运营商、开发商、提供商和当局。我们评估了5G和5G- advanced系统的孤岛准备情况，并概述了利益相关者必须解决的开放挑战，以实现完全的孤岛准备，例如分散6G核心网络和设计本地优先的应用架构。

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引用次数: 0

MetaHeart: Metasurface enabled biometrics camouflage MetaHeart: metassurface启用生物识别伪装

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-27 DOI: 10.1016/j.comcom.2025.108405

Dora Zivanovic , Jy-Chin Liao , Zhambyl Shaikhanov , Hou-Tong Chen , Chun-Chieh Chang , Sadhvikas Addamane , Daniel M. Mittleman , Edward W. Knightly

Privacy-invading biometrics monitoring is becoming a prominent security threat as modern sensing systems move to higher operating frequencies (mmWave, sub-THz), increasing sensing resolution and accuracy. As such, developing systems that can protect or obfuscate biometrics from adversarial intrusion becomes pivotal to preserving user privacy. In this work, we develop and implement MetaHeart, a real-time biometrics misinformation system based on reflective, programmable metasurfaces and dynamic phase-front manipulation of radar inferences. MetaHeart’s key goal is to prevent the leakage of a legitimate user’s heartbeat biometrics by spoofing fake heartbeat signals at a malicious, radar-equipped, heart rate sensing intruder. We experimentally demonstrate MetaHeart’s ability to fake Alice’s presence when she is not there and to fool Trudy’s inferences even when Alice is present, achieving an overall accuracy above 98%. Finally, we conduct a robustness analysis to determine MetaHeart’s required spatial placement within the intruder’s monitoring area that would allow for effective spoofing.

随着现代传感系统向更高的工作频率（毫米波、次太赫兹）移动，传感分辨率和精度不断提高，侵犯隐私的生物识别监测正成为一个突出的安全威胁。因此，开发能够保护或混淆生物识别技术免受敌对入侵的系统对于保护用户隐私至关重要。在这项工作中，我们开发并实现了MetaHeart，这是一个基于反射、可编程元表面和雷达推断的动态相位前操作的实时生物识别错误信息系统。MetaHeart的主要目标是通过欺骗伪造的心跳信号，防止合法用户的心跳生物识别信息泄露给恶意的、配备雷达的心率感应入侵者。我们通过实验证明，MetaHeart能够在爱丽丝不在场的时候假装她的存在，即使在爱丽丝在场的时候也能欺骗特鲁迪的推断，总体准确率超过98%。最后，我们进行了鲁棒性分析，以确定MetaHeart在入侵者监控区域内所需的空间位置，从而允许有效的欺骗。

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引用次数: 0

Making TCP IoT-friendly towards the 6G era 面向6G时代，使TCP物联网友好

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-27 DOI: 10.1016/j.comcom.2025.108406

Carles Gomez , Jon Crowcroft

Traditionally, Internet of Things (IoT) communication technologies have been designed to offer low bit rates (from ∼10² to ∼10⁶ bit/s). However, recent IoT-intended technologies like 5G Reduced Capability (RedCap) support significantly greater bit rates (up to ∼10⁸ bit/s), enabling emerging IoT use cases that demand greater capacity. Thus, the spectrum of IoT scenarios and corresponding requirements is expanding, a trend which is expected to continue with 6G networks. In this context, support, configuration and performance of a crucial upper-layer protocol like TCP become challenging. In this paper, based on our IETF standardization work, we describe how TCP can run suitably on a wide variety of IoT environments (from highly constrained scenarios to resource-rich ones). Furthermore, we present and study the novel TCP option called TCP Acknowledgment Rate Request (TARR), designed for further TCP adaptability, which is particularly useful for current and future IoT networks.

传统上，物联网（IoT）通信技术被设计为提供低比特率（从~ 102到~ 106比特/秒）。然而，最近的物联网技术，如5G低容量（RedCap），支持更高的比特率（高达~ 108比特/秒），使新兴的物联网用例需要更大的容量。因此，物联网场景的范围和相应的需求正在扩大，预计6G网络将继续这一趋势。在这种情况下，像TCP这样重要的上层协议的支持、配置和性能变得具有挑战性。在本文中，基于我们的IETF标准化工作，我们描述了TCP如何在各种物联网环境（从高度受限的场景到资源丰富的场景）上适当地运行。此外，我们提出并研究了一种新的TCP选项，称为TCP确认率请求（TARR），旨在进一步提高TCP的适应性，这对当前和未来的物联网网络特别有用。

引用次数: 0

Joint optimization of UAV trajectory, RIS selection, and offloading strategy for RIS-assisted UAV-MEC systems based on DRL 基于DRL的RIS辅助UAV- mec系统航迹、RIS选择及卸载策略联合优化

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-24 DOI: 10.1016/j.comcom.2025.108402

Jianhua Liu , Bo Tang , Jiajia Liu , Xia Lei , Xiaoguang Tu , Xiaofan Wang

The Mobile Edge Computing (MEC) system equipped with servers on Unmanned Aerial Vehicle (UAV) is an effective solution for computing offloading in the absence of communication resource infrastructure. Introducing Reconfigurable Intelligent Surfaces (RIS) into the UAV-MEC system can enhance air-to-ground communication quality and optimize Non-Line-of-Sight (N-LoS) links. However, how to dynamically select service users for the RIS based on environmental changes, so as to reduce signal interference and improve resource utilization, remains a key challenge to be addressed. To tackle this issue, we aim to jointly optimize the three-dimensional (3D) trajectory of UAV, RIS selection decisions, and task offloading strategy to minimize the overall system delay. Due to the non-convexity of the joint problem, it is difficult to solve it efficiently using traditional optimization methods. Therefore, this paper proposes a Hybrid Exploration Deep Deterministic Policy Gradient (HEDDPG) algorithm based on Deep Reinforcement Learning (DRL). By integrating random exploration into the original DDPG framework, the proposed algorithm enhances global search capability in complex environments. The experimental results show that with the addition of RIS assistance, the system delay is reduced by 18.8%. Compared to other benchmark algorithms, HEDDPG performs better, achieving efficient strategy optimization for the system and improving the performance of communication links and resource utilization efficiency.

在无人机（UAV）上配备服务器的移动边缘计算（MEC）系统是在没有通信资源基础设施的情况下进行计算卸载的有效解决方案。将可重构智能表面（RIS）引入无人机- mec系统可以提高空对地通信质量并优化非视距（N-LoS）链路。然而，如何根据环境变化动态选择RIS的业务用户，以减少信号干扰，提高资源利用率，仍然是一个需要解决的关键挑战。为了解决这一问题，我们的目标是共同优化无人机的三维（3D）轨迹，RIS选择决策和任务卸载策略，以最小化整个系统的延迟。由于关节问题的非凸性，传统的优化方法难以有效求解。为此，本文提出了一种基于深度强化学习（DRL）的混合探索深度确定性策略梯度（HEDDPG）算法。该算法将随机搜索集成到原始DDPG框架中，增强了复杂环境下的全局搜索能力。实验结果表明，加入RIS辅助后，系统延迟降低了18.8%。与其他基准算法相比，HEDDPG性能更好，实现了系统高效的策略优化，提高了通信链路性能和资源利用效率。

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引用次数: 0

A cooperative distributed model to evaluate and optimize task offloading in Mobile Edge Computing 移动边缘计算中任务卸载评估与优化的协作分布式模型

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-20 DOI: 10.1016/j.comcom.2025.108403

Fabrizio Messina , Domenico Rosaci

This paper proposes a cooperative and distributed framework to evaluate and optimize task offloading in Mobile Edge Computing (MEC). Each agent, representing either a user device or an edge domain, autonomously interacts with others through trust-driven recommendations and cluster formation. The proposed algorithm exploits this information to iteratively increase – and asymptotically converge over time to – the configuration that maximizes the collective utility of edge servers and user devices, i.e., the Average Performance (

A P

), which corresponds to a Nash equilibrium where only reliable agents are rewarded. Two synthetic indicators are introduced to model the main aspects of MEC collaboration: the Quality of Experience (

Q o E

), representing the perceived user-side performance, and the Convenience (

C

), expressing the server-side efficiency and resource cost. Experimental validation, performed over a simulated MEC environment with up to 1000 agents, shows a rapid convergence (within 20 iterations), a stable equilibrium with

A P \approx 0.92

, and robustness to variations in the simulated values of agents’ reliability. The results demonstrate that the proposed distributed algorithm achieves efficient, self-organized coordination among heterogeneous edge entities while maintaining scalability and fairness.

提出了一种协作式分布式框架来评估和优化移动边缘计算（MEC）中的任务卸载。每个代理代表一个用户设备或一个边缘域，通过信任驱动的建议和集群形成自主地与其他代理交互。所提出的算法利用这些信息迭代地增加-并随着时间的推移渐近收敛-最大化边缘服务器和用户设备的集体效用的配置，即平均性能（AP），这对应于纳什均衡，其中只有可靠的代理得到奖励。引入了两个综合指标来对MEC协作的主要方面进行建模：体验质量（QoE），代表感知到的用户端性能，以及便利性(C)，表示服务器端效率和资源成本。在多达1000个代理的模拟MEC环境中进行的实验验证表明，该方法具有快速收敛（在20次迭代内）、稳定的平衡（AP≈0.92）以及对代理可靠性模拟值变化的鲁棒性。结果表明，该算法在保持可扩展性和公平性的同时，实现了异构边缘实体之间高效、自组织的协调。

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引用次数: 0

Power prediction and energy aware placement of containers over virtual machines 在虚拟机上对容器进行功率预测和能源感知

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-17 DOI: 10.1016/j.comcom.2025.108340

Rafael Albuquerque, Brigitte Jaumard

The rapid expansion of 5G and the upcoming arrival of 6G have significantly increased the demand for cloud computing resources, especially in edge cloud servers, to meet stringent connectivity and latency requirements. This surge has raised serious energy concerns as data centers now account for about 1–1.5% of global energy consumption and contribute about 1% of global CO

_{2}

emissions. In response to these facts, this study proposes a novel energy-aware machine learning model, using power sensor data from physical machines (PMs) in data centers, to optimize energy consumption while managing container placement as a use case.

We conducted experiments in a testbed using realistic 5G traffic scenarios, deliberately avoiding artificial stressors such as stress-ng, which create synthetic loads that do not accurately reflect real-world resource utilization. Our machine learning model, particularly the XGBoost implementation, proved to be highly effective, achieving an

R^{2}

score of 91.2%. The model demonstrated the ability to reduce energy consumption by 3% and improve task completion times, all without the need for explicit consolidation strategies or cluster reconfiguration.

This approach highlights the power of machine learning in optimizing energy efficiency in dynamic and resource-intensive environments such as edge cloud servers, providing a scalable solution for data centers facing increasing energy demands.

5G的快速扩张和即将到来的6G大幅增加了对云计算资源的需求，特别是在边缘云服务器方面，以满足严格的连接和延迟要求。这种激增引起了严重的能源问题，因为数据中心目前约占全球能源消耗的1-1.5%，并贡献了约1%的全球二氧化碳排放量。针对这些事实，本研究提出了一种新的能源感知机器学习模型，该模型使用数据中心物理机器（pm）的功率传感器数据，以优化能源消耗，同时管理容器放置作为用例。我们在一个使用真实5G流量场景的测试平台上进行了实验，刻意避免了压力-ng等人为压力源，这些压力源会产生无法准确反映真实资源利用率的合成负载。我们的机器学习模型，特别是XGBoost实现，被证明是非常有效的，达到了91.2%的R2分数。该模型表明，在不需要明确的整合策略或集群重新配置的情况下，该模型能够将能耗降低3%，并提高任务完成时间。这种方法突出了机器学习在动态和资源密集型环境（如边缘云服务器）中优化能源效率的强大功能，为面临日益增长的能源需求的数据中心提供了可扩展的解决方案。

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引用次数: 0