IEEE Transactions on Sustainable Computing最新文献_第3页

Reliability-Driven SOP Siting and Sizing Under Multi-Line Failure Scenarios in Resource-Complementary Island Microgrids 资源互补型孤岛微电网多线路故障情景下可靠性驱动SOP选址与规模研究

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-03-12 DOI: 10.1109/TSUSC.2026.3673142

Ziyao Wang;Yingrui Zhao;Wei Chen;Yusi Cheng;Jimmy Chih-Hsien Peng

{"title":"Reliability-Driven SOP Siting and Sizing Under Multi-Line Failure Scenarios in Resource-Complementary Island Microgrids","authors":"Ziyao Wang;Yingrui Zhao;Wei Chen;Yusi Cheng;Jimmy Chih-Hsien Peng","doi":"10.1109/TSUSC.2026.3673142","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3673142","url":null,"abstract":"Island microgrids face major challenges arising from the spatial mismatch between renewable generation and load centers as well as their heightened susceptibility to line failures. Traditional strategies such as full-line reinforcement are often impractical for islands due to the extremely high investment cost. This paper proposes a reliability-driven planning framework for microgrids under various failure scenarios, introducing Soft Open Points (SOPs) as flexible interconnection devices. Line failure rates are estimated based on component health indices and submarine cable lengths, and a scenario-based mixed-integer linear programming (MILP) model is developed to jointly optimize distributed energy resource scheduling and SOP siting and sizing. The proposed framework is validated on the Orkney Islands microgrid. Case studies demonstrate that optimal SOP deployment can reduce load shedding and renewable curtailment, improve voltage profiles, and lower total operational costs by over 50%, without the need for large-scale infrastructure upgrades. The results provide practical insights into enhancing the reliability and cost-effectiveness of island microgrids under reliability constraints.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"240-252"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Aware VM Consolidation Using Similarity-Driven Intelligence in Green Cloud Environments 绿色云环境中使用相似驱动智能的能源感知VM整合

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1109/TSUSC.2026.3660014

Nirmal Kr Biswas;Debashis Das;Sourav Banerjee;Utpal Biswas

{"title":"Energy-Aware VM Consolidation Using Similarity-Driven Intelligence in Green Cloud Environments","authors":"Nirmal Kr Biswas;Debashis Das;Sourav Banerjee;Utpal Biswas","doi":"10.1109/TSUSC.2026.3660014","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3660014","url":null,"abstract":"Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today’s world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"123-134"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward a More Effective and Comprehensive Assessment of Data Center Environmental Impact 迈向更有效和全面的数据中心环境影响评估

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-20 DOI: 10.1109/TSUSC.2026.3666670

Dina Nassar;Rabih Bashroush

{"title":"Toward a More Effective and Comprehensive Assessment of Data Center Environmental Impact","authors":"Dina Nassar;Rabih Bashroush","doi":"10.1109/TSUSC.2026.3666670","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666670","url":null,"abstract":"The escalating demand for data and computational power has led to the rapid expansion of data centres, significantly increasing their environmental footprint. Data centres now account for an estimated 2% to 4% of global electricity consumption. Although various best practices and key performance indicators (KPIs) have been developed to mitigate this impact, creating a comprehensive framework for evaluating the environmental sustainability of data centres remains challenging. Existing certification schemes, such as Leadership in Energy and Environmental Design (LEED) and the Building Research Establishment Environmental Assessment Method (BREEAM), were originally designed for traditional building infrastructure and are increasingly applied to data centres. However, these frameworks primarily focus on building-related aspects, inadequately addressing the unique mechanical, electrical, and IT dimensions critical to data centres. This study introduces a comprehensive evaluation model applied to the LEED framework, identifying significant gaps and proposing enhancements. The findings highlight notable discrepancies and limitations within the current LEED version, emphasising the need for tailored approaches that accurately reflect the distinct characteristics and operational demands of data centres.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"187-198"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11404237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eco-Friendly and Explainable Hybrid Machine Learning Models for Sustainable Crop and Fertilizer Recommendation System 可持续作物和肥料推荐系统的生态友好和可解释的混合机器学习模型

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-04-28 DOI: 10.1109/TSUSC.2026.3688560

Abhirup Paria;Sarmistha Neogy;Arindam Giri;Subrata Dutta;Pradorn Sureephong;Sri Devi Ravana

{"title":"Eco-Friendly and Explainable Hybrid Machine Learning Models for Sustainable Crop and Fertilizer Recommendation System","authors":"Abhirup Paria;Sarmistha Neogy;Arindam Giri;Subrata Dutta;Pradorn Sureephong;Sri Devi Ravana","doi":"10.1109/TSUSC.2026.3688560","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3688560","url":null,"abstract":"This work proposes a sustainable crop and fertilizer recommendation system using eco-friendly hybrid machine learning (EHML) and explainable artificial intelligence (XAI) models. The integration of EHML with XAI is introduced for the first time in the crop and fertilizer recommendation system. This work focuses on crop recommendations solely based on the micro and macro nutrients present in the soil, an aspect that has been largely overlooked till date. Based on various hybrid machine learning (HML) models such as CatBoost and Extreme Gradient Boosting, optimized with Sparrow Search Algorithm, Phasor Particle Swarm Optimization, and Grey Wolf Optimization, crops are recommended with accuracy ranging from 99.64% to 99.87%. The state-of-the-art work is also compared with hybrid models optimized with standard optimizers, such as Genetic Algorithm and Particle Swarm Optimization. To reveal critical drivers of crop selection and enhance the interpretability of the best-performed models, XAI algorithms (Local Interpretable Model Explanation and SHapley Additive exPlanations) are employed. The best-performing eco-friendly model is also recommended based on low <inline-formula><tex-math>${CO}_{2}eq$</tex-math></inline-formula> emissions and electricity consumption, helping to preserve natural resources. Additionally, a farmer-friendly web application is developed to provide instant crop and fertilizer recommendations for sustainable agriculture. Finally, the recommended fertilizers are also validated using real-time field data collected via IoT sensors.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"291-301"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Cluster-Based Knowledge Distillation for Deep Face Recognition 基于聚类的深度人脸识别知识精馏方法

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-03-26 DOI: 10.1109/TSUSC.2026.3677896

Xianwei Lv;Haibo Mi;Xin Niu;Wang Chen;Kun Wang;Chen Yu

{"title":"Efficient Cluster-Based Knowledge Distillation for Deep Face Recognition","authors":"Xianwei Lv;Haibo Mi;Xin Niu;Wang Chen;Kun Wang;Chen Yu","doi":"10.1109/TSUSC.2026.3677896","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3677896","url":null,"abstract":"Knowledge distillation has been widely used to improve the performance of small compact models for face recognition. However, selecting key knowledge and effectively transferring it from teacher to student remains a challenging problem. In this work, we propose an efficient Cluster-based Knowledge Distillation (CKD) dedicated to aligning the student model with the teacher model in terms of both sample relations and class centers. Specifically, CKD first determines the key sample relations based on the similarities between the sample features extracted by the teacher and their cluster centers generated by existing clustering algorithms. Then, CKD effectively transfers the knowledge of the above relations from the teacher to the student by designing a cluster-based relation distillation loss. Finally, CKD further improves the quality of the student's class centers by constructing a center loss between the above representative cluster centers and the student's class centers. We validate the proposed CKD on multiple face benchmarks. For example, CKD improves the baseline student performance from 91.95% to 94.20% on MegaFace and consistently outperforms recent competitive distillation methods on multiple benchmarks. These results demonstrate the effectiveness and superiority of CKD.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"253-265"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BEExformer: A Fast Inferencing Binarized Transformer With Early Exits BEExformer：一种具有早期出口的快速推理二值化变压器

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1109/TSUSC.2026.3666456

Wazib Ansar;Saptarsi Goswami;Amlan Chakrabarti

{"title":"BEExformer: A Fast Inferencing Binarized Transformer With Early Exits","authors":"Wazib Ansar;Saptarsi Goswami;Amlan Chakrabarti","doi":"10.1109/TSUSC.2026.3666456","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666456","url":null,"abstract":"Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements hinder deployment on constrained resources. To enhance efficiency, binarization and Early Exit (EE) have proved to be effective solutions. However, binarization may lead to performance loss as reduced precision affects gradient estimation and parameter updates. Besides, research on EE mechanisms is still in its early stages. To address these challenges, we introduce Binarized Early Exit Transformer (BEExformer), a first-of-its-kind selective learning-based transformer integrating Binarization-Aware Training (BAT) with EE for efficient and fast textual inference. Each transformer block has an integrated Selective-Learn Forget Network (SLFN) to enhance contextual retention while eliminating irrelevant information. The BAT employs a differentiable second-order approximation to the sign function, enabling gradient computation that captures both the sign and magnitude of the weights. This aids in 21.30 × reduction in model size. The EE mechanism hinges on fractional reduction in entropy among intermediate transformer blocks with soft-routing loss estimation. This accelerates inference by reducing FLOPs by 52.27% and even improves accuracy by 3.22% by resolving the “overthinking” problem inherent in deep networks. Extensive evaluation through comparison with the SOTA methods and various ablations across nine datasets covering multiple NLP tasks demonstrates its Pareto-optimal performance-efficiency trade-off.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"98-110"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning Veri-SFL：可持续联邦学习中资源分配和数据可信度的隐私保护验证

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1109/TSUSC.2026.3653218

Yu-Chi Chen;You-Siang Liao;Zong-Sian Lai

{"title":"Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning","authors":"Yu-Chi Chen;You-Siang Liao;Zong-Sian Lai","doi":"10.1109/TSUSC.2026.3653218","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3653218","url":null,"abstract":"Federated Learning (FL) is currently referred to as one of the privacy-enhancing technologies because of its service architecture. However, recent advancements in FL have highlighted its potential not only as a new framework of privacy but also as a key enabler of sustainable computing, which is expected to minimize the impact of an individual party to further improve the capacity of the machine learning model, energy efficiency, and reliability. For the above requirement of sustainability, resource allocation and trust management in FL are very infrastructural tasks of energy efficiency and reliability. In this paper, we present a framework, called Veri-SFL, to indicate verification for resource allocation and trust measurement in FL. We use trust scores to represent the credibility of each dataset without leaking any privacy, and utilize collaborative zk-SNARKs to verify the trust scores of each local dataset. Then, after verifying the correctness of trust levels, we present a solution to verify whether workers (model owners) are training according to the required distribution ratio by using collaborative zk-SNARKs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"199-212"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

iClean: An Intelligent Industrial IoT Framework for Automatic Sustainable Air Quality Monitoring iClean：用于自动可持续空气质量监测的智能工业物联网框架

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1109/TSUSC.2026.3653174

Lopamudra Samal;Aryan Kumar Samal;Kamalakanta Mahapatra;Ayas Kanta Swain;Saraju P. Mohanty

{"title":"iClean: An Intelligent Industrial IoT Framework for Automatic Sustainable Air Quality Monitoring","authors":"Lopamudra Samal;Aryan Kumar Samal;Kamalakanta Mahapatra;Ayas Kanta Swain;Saraju P. Mohanty","doi":"10.1109/TSUSC.2026.3653174","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3653174","url":null,"abstract":"Air pollution monitoring systems are essential for evaluating rural and industrial environmental quality for safeguarding public health. This study presents a comprehensive IoT-based framework that uses low-cost sensors and machine learning algorithms for real-time monitoring of various pollutants, including LPG, methane, CO, alcohol, PM2.5, PM10, temperature, and humidity. The system gathers sensor data from a gateway node, which is then processed using Support Vector Machines (SVM) and Random Forest Regression (RFR) models to predict pollutant concentrations. Our approach features innovative methodologies for data validation, anomaly detection, and predictive modeling, employing Root Mean Squared Error (RMSE) as the performance metric. The model achieved a remarkably low RMSE value of 0.022, significantly improving the accuracy and reliability of air quality assessments. Experimental results highlight the system’s capability to capture complex environmental patterns and predict pollutant levels with high precision. This research intends air pollution monitoring from cost-effective Internet of Things (IoT) solutions and machine learning techniques. Additionally, the user interface is designed for mobile applications, offering real-time data access, alerts, and notifications, thereby enabling personalized environmental health management and targeted pollution control strategies in industrial areas.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"135-146"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAPPO-Driven Task Quality Optimization With Wireless Energy Transfer in Multi-UAV MEC-Enabled IoT Networks 多无人机mec - IoT网络中基于无线能量传输的mappo驱动任务质量优化

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-04-29 DOI: 10.1109/TSUSC.2026.3688744

Nayanjit Talukdar;Tushar Barai;Abhishek Hazra;Nabajyoti Mazumdar

{"title":"MAPPO-Driven Task Quality Optimization With Wireless Energy Transfer in Multi-UAV MEC-Enabled IoT Networks","authors":"Nayanjit Talukdar;Tushar Barai;Abhishek Hazra;Nabajyoti Mazumdar","doi":"10.1109/TSUSC.2026.3688744","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3688744","url":null,"abstract":"Multi-UAV systems are increasingly essential to Internet of Things (IoT) networks, offering agile platforms for data collection, Wireless Energy Transfer (WET), and edge computing. Although significant progress has been made in UAV-enabled architectures, persistent challenges remain due to stringent energy constraints, dynamic task demands, and the high-dimensional nature of multi-UAV coordination. This work seeks to optimize deadline-aware task quality in battery-constrained IoT networks using Mobile Edge Computing (MEC)-enabled UAVs, each assigned Core Tasks with strict deadlines and Supplementary Tasks to enhance quality when additional resources are available. Nevertheless, the joint optimization of UAV 3D trajectories, WET, and task scheduling under tight energy and deadline constraints introduces a dynamic and continuous decision-making challenge. To address this, we propose a novel multi-UAV framework built upon two key pillars: (i) a Beta-distributed policy network for modeling bounded, continuous control actions, and (ii) a multi-head attention-based critic for capturing inter-UAV interactions and global context. Accordingly, we formulate the problem as a multi-agent Markov Decision Process (MDP) and develop Beta-Attention Multi-Agent Proximal Policy Optimization (BA-MAPPO), a Deep Reinforcement Learning (DRL) framework based on Multi-Agent Proximal Policy Optimization. Extensive simulations demonstrate that BA-MAPPO substantially improves energy efficiency, task quality, and deadline compliance, outperforming existing baselines in dynamic IoT environments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"302-315"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Spatiotemporal Optimal Scheduling for Distributed Data Centers With Multiple Uncertainties 多不确定性分布式数据中心鲁棒时空最优调度

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1109/TSUSC.2026.3665882

Jianqiang Hu;Jiuan Lu;Josep M. Guerrero;Jinde Cao

引用次数: 0