IEEE Transactions on Sustainable Computing最新文献_第4页

Design Workload Aware Data Collection Technique for IoT-enabled WSNs in Sustainable Smart Cities 可持续智慧城市中支持物联网的wsn的工作负载感知数据收集技术设计

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-24 DOI: 10.1109/TSUSC.2024.3418136

Walid Osamy;Ahmed M. Khedr;Ahmed Salim

{"title":"Design Workload Aware Data Collection Technique for IoT-enabled WSNs in Sustainable Smart Cities","authors":"Walid Osamy;Ahmed M. Khedr;Ahmed Salim","doi":"10.1109/TSUSC.2024.3418136","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3418136","url":null,"abstract":"Load balancing in IoT-based Wireless Sensor Networks (WSNs) is essential for improving energy efficiency, reliability, and network lifetime, promoting the development of smart and sustainable cities through informed decision-making and resource optimization. This paper introduces a Workload Aware Clustering Technique (WLACT) to enhance energy efficiency and extend the network lifespan of IoT-based WSNs. WLACT focuses on overcoming challenges such as uneven workload distribution and complex scheme designs in existing clustering methods, highlighting the importance of load balancing, optimized data aggregation, and effective energy resource management in IoT-based heterogeneous WSNs. WLACT adapts Chicken Swarm Optimization (CSO) for efficient workload-aware clustering of WSNs, while also introducing the concept of average imbalanced workload parameter for clustered WSNs and utilizing it as an evaluation metric. By considering node heterogeneity and formulating an objective function to minimize workload imbalances among nodes during clustering, WLACT aims to achieve efficient energy resource utilization, improved reliability, and long-term operational support within smart city environments. A new cluster joining procedure for non-CHs based on multiple factors is also designed. Results reveal the superior performance of WLACT in terms of energy efficiency, workload balance, reliability, and network lifetime, making it a promising technique for sustainable smart city development.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"244-261"},"PeriodicalIF":3.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769402","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

Merged Path: Distributed Data Dissemination in Mobile Sinks Sensor Networks 合并路径：移动汇传感器网络中的分布式数据传播

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-06 DOI: 10.1109/TSUSC.2024.3410247

Xingfu Wang;Ammar Hawbani;Liang Zhao;Saeed Hamood Alsamhi;Wajdy Othman;Mohammed A.A. Al-qaness;Alexey V. Shvetsov

{"title":"Merged Path: Distributed Data Dissemination in Mobile Sinks Sensor Networks","authors":"Xingfu Wang;Ammar Hawbani;Liang Zhao;Saeed Hamood Alsamhi;Wajdy Othman;Mohammed A.A. Al-qaness;Alexey V. Shvetsov","doi":"10.1109/TSUSC.2024.3410247","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3410247","url":null,"abstract":"This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between the <italic>high-tier</i> and low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"161-175"},"PeriodicalIF":3.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184133","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

IOTA-Based Game-Theoretic Energy Trading With Privacy-Preservation for V2G Networks 基于iota的V2G网络隐私保护博弈论能源交易

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-06 DOI: 10.1109/TSUSC.2024.3410237

Muhammad Rizwan;Mudassir Ali;Ammar Hawbani;Wang Xingfu;Adeel Anjum;Pelin Angin;Olaoluwa Popoola;Muhammad Ali Imran

{"title":"IOTA-Based Game-Theoretic Energy Trading With Privacy-Preservation for V2G Networks","authors":"Muhammad Rizwan;Mudassir Ali;Ammar Hawbani;Wang Xingfu;Adeel Anjum;Pelin Angin;Olaoluwa Popoola;Muhammad Ali Imran","doi":"10.1109/TSUSC.2024.3410237","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3410237","url":null,"abstract":"Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"217-231"},"PeriodicalIF":3.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769498","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

An Efficient DDoS Detection Method Based on Packet Grouping via Online Data Flow Processing 基于在线数据流处理分组的高效DDoS检测方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-05 DOI: 10.1109/TSUSC.2024.3409712

Mingshu He;Xiaowei Zhao;Xiaojuan Wang

{"title":"An Efficient DDoS Detection Method Based on Packet Grouping via Online Data Flow Processing","authors":"Mingshu He;Xiaowei Zhao;Xiaojuan Wang","doi":"10.1109/TSUSC.2024.3409712","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3409712","url":null,"abstract":"Distributed Denial of Service attacks are considered to be one of the most common and effective threats in the security field, aiming to deny or weaken the service providing of its victims. Most traditional solutions are only for DDoS detection in offline scenarios, which are challenging to detect real-time DDoS attacks. Therefore, the application scenarios are limited. In this paper, we propose a packet grouping-based DDoS detection method, which uses an online data flow processing mechanism to focus on data collection and processing efforts, which is suitable for online and offline detection. The proposed method simulates the process of real-time packet capture by grouping packets through a time window and realizes the binary classification of traffic through the lightweight CNN model. Most crucially, selecting the optimal number of packets per time window minimizes the time overhead without affecting detection accuracy. To further improve the accuracy in offline scenarios, we perform ensemble learning on the prediction results of packet groups. The proposed method attains 99.99<inline-formula><tex-math>$%$</tex-math></inline-formula> accuracy on the CICIDS2017 offline dataset and demonstrates a latency of only 1.05 seconds with a 99.86<inline-formula><tex-math>$%$</tex-math></inline-formula> accuracy in online testing, surpassing other methods in terms of response speed.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"202-216"},"PeriodicalIF":3.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769496","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

Safeguarding Patient Data-Sharing: Blockchain-Enabled Federated Learning in Medical Diagnostics 保障患者数据共享：医疗诊断中的区块链联合学习

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-04 DOI: 10.1109/TSUSC.2024.3409329

Raushan Myrzashova;Saeed Hamood Alsamhi;Ammar Hawbani;Edward Curry;Mohsen Guizani;Xi Wei

{"title":"Safeguarding Patient Data-Sharing: Blockchain-Enabled Federated Learning in Medical Diagnostics","authors":"Raushan Myrzashova;Saeed Hamood Alsamhi;Ammar Hawbani;Edward Curry;Mohsen Guizani;Xi Wei","doi":"10.1109/TSUSC.2024.3409329","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3409329","url":null,"abstract":"Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients’ medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112,120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10,034,017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"176-189"},"PeriodicalIF":3.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184140","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

bSlight 2.0: Battery-Free Sustainable Smart Street Light Management System blight 2.0：无电池可持续智能路灯管理系统

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-03 DOI: 10.1109/TSUSC.2024.3408630

Prajnyajit Mohanty;Umesh C. Pati;Kamalakanta Mahapatra;Saraju P. Mohanty

{"title":"bSlight 2.0: Battery-Free Sustainable Smart Street Light Management System","authors":"Prajnyajit Mohanty;Umesh C. Pati;Kamalakanta Mahapatra;Saraju P. Mohanty","doi":"10.1109/TSUSC.2024.3408630","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3408630","url":null,"abstract":"Street lighting is one of the prominent applications that demand a massive amount of power and substantially contributes to the energy budget of a country. Light Emitting Diode (LED) and the advancement of Internet of Things (IoT) have significantly improved conventional street light technology. Nevertheless, the rapid growth of IoT devices has presented a formidable challenge in powering the vast array of IoT devices. In this manuscript, a sustainable, battery-free, low-power street light management system has been proposed which is powered from hybrid solar and solar thermal energy harvesting scheme integrated with an efficient power management unit. As a specific case study, the prototype has been implemented with an existing LED street light in India. The characteristics and performance of the prototype have been evaluated to ensure its seamless operation under real-world scenarios. The average power consumption of the system is measured as 2.088 mW when operating in real-time with 50% duty cycle, exhibiting high Quality of Service (QoS). It features long-range communication up to 761 m through implementing LoRaWAN technology. Dimension of the prototype has been restricted to 10.5 cm × 6.5 cm × 2.3 cm to make it suitable for retrofitting with existing LED based street lights.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"146-160"},"PeriodicalIF":3.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184037","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

Addressing Concept Drift in IoT Anomaly Detection: Drift Detection, Interpretation, and Adaptation 物联网异常检测中的寻址概念漂移：漂移检测，解释和适应

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-26 DOI: 10.1109/TSUSC.2024.3386667

Lijuan Xu;Ziyu Han;Dawei Zhao;Xin Li;Fuqiang Yu;Chuan Chen

{"title":"Addressing Concept Drift in IoT Anomaly Detection: Drift Detection, Interpretation, and Adaptation","authors":"Lijuan Xu;Ziyu Han;Dawei Zhao;Xin Li;Fuqiang Yu;Chuan Chen","doi":"10.1109/TSUSC.2024.3386667","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3386667","url":null,"abstract":"Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT (Internet of Things), changes in system configurations and the introduction of new devices can lead to significant alterations in device relationships and data flows within the IoT, thereby triggering concept drift. Previously trained anomaly detection models fail to adapt to the changed distribution of streaming data, resulting in a high number of false positive events. This paper aims to address the issue of concept drift in IoT anomaly detection by proposing a comprehensive Concept Drift Detection, Interpretation, and Adaptation framework (CDDIA). We focus on accurately capturing the concept drift of normal data in unsupervised scenarios. To interpret drift samples, we integrate a search optimization algorithm and the SHAP method, providing a comprehensive interpretation of drift samples at both the sample and feature levels. Simultaneously, by utilizing the sample-level interpretation results for filtering new and old samples, we retrain the anomaly detection model to mitigate the impact of concept drift and reduce the false positive rate. This integrated strategy demonstrates significant advantages in maintaining model stability and reliability. The experimental results indicate that our method outperforms five baseline methods in adaptability across three datasets and provides interpretability for samples experiencing concept drift.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"913-924"},"PeriodicalIF":3.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810508","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

An Intelligent Straggler Traffic Management Framework for Sustainable Cloud Environments 面向可持续云环境的智能滞留者流量管理框架

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-24 DOI: 10.1109/TSUSC.2024.3393357

Smruti Rekha Swain;Deepika Saxena;Jatinder Kumar;Ashutosh Kumar Singh;Chung-Nan Lee

{"title":"An Intelligent Straggler Traffic Management Framework for Sustainable Cloud Environments","authors":"Smruti Rekha Swain;Deepika Saxena;Jatinder Kumar;Ashutosh Kumar Singh;Chung-Nan Lee","doi":"10.1109/TSUSC.2024.3393357","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3393357","url":null,"abstract":"Large-scale computing systems in the modern era distribute tasks into smaller units that can be executed simultaneously to speed up job completion and decrease energy usage. However, cloud computing systems encounter a significant challenge called the Long Tail problem, where a small subset of slow-performing tasks hinders the overall progress of parallel job execution. This behavior leads to longer service response times and reduced system efficiency. This paper introduces a novel approach called Stochastic Gradient Descent with Momentum-driven Neural Network to analyze and classify heterogeneous tasks as either stragglers or non-stragglers. The straggler tasks are further categorized into Resource Hunter and Long-Tail stragglers based on their specific resource requirements. A traffic management policy is implemented to schedule and assign resources among user job requests, considering the task category, to achieve parallelism and improve sustainability within the cloud infrastructure. Extensive simulations are conducted using the Google Cluster Dataset (GCD) to assess the effectiveness of the proposed framework. The results obtained from these simulations are then compared to state-of-the-art techniques. The experimental findings demonstrate significant reductions in power consumption, carbon emissions, active servers, conflicting servers, and VM migration up to 55.16%, 49.76%, 35%, 25.7%, and 87.29%, respectively. Moreover, there has been an enhancement in resource utilization by up to 78.31%, accompanied by a decrease in execution time of up to 67.74%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"82-94"},"PeriodicalIF":3.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184032","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

Qin: Unified Hierarchical Cluster-Node Scheduling for Heterogeneous Datacenters 秦：异构数据中心的统一分层集群节点调度

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-22 DOI: 10.1109/TSUSC.2024.3392480

Wenkai Guan;Cristinel Ababei

{"title":"Qin: Unified Hierarchical Cluster-Node Scheduling for Heterogeneous Datacenters","authors":"Wenkai Guan;Cristinel Ababei","doi":"10.1109/TSUSC.2024.3392480","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3392480","url":null,"abstract":"Energy efficiency is among the most important challenges for computing. There has been an increasing gap between the rate at which the performance of processors has been improving and the lower rate of improvement in energy efficiency. This paper answers the question of how to reduce energy usage in heterogeneous datacenters. It proposes a unified hierarchical scheduling using a D-Choices technique, which considers interference and heterogeneity. Heterogeneity comes from servers’ continuous upgrades and the integrated high-performance “big” and energy-efficient “little” cores. This results in datacenters becoming more heterogeneous and traditional job scheduling algorithms become suboptimal. To this end, we present a two-level hierarchical scheduler for datacenters that exploits increased server heterogeneity. It combines in a unified approach cluster and node level scheduling algorithms, and it can consider specific optimization objectives including job completion time, energy usage, and energy-delay-product (EDP). Its novelty lies in the unified approach and in modeling interference and heterogeneity. Experiments on a research cluster found that the proposed approach outperforms state-of-the-art schedulers by around 10% in job completion time, 39% in energy usage, and 42% in EDP. This paper demonstrated a unified approach as a promising direction in optimizing energy and performance for heterogeneous datacenters.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"39-56"},"PeriodicalIF":3.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184028","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

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers CFWS：基于 DRL 的云数据中心能源成本与碳足迹优化框架

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-04-22 DOI: 10.1109/TSUSC.2024.3391791

Daming Zhao;Jian-tao Zhou;Keqin Li

{"title":"CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers","authors":"Daming Zhao;Jian-tao Zhou;Keqin Li","doi":"10.1109/TSUSC.2024.3391791","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391791","url":null,"abstract":"The rapid growth and widespread adoption of cloud computing have led to significant electricity costs and environmental impacts. Traditional approaches that rely on static utilization thresholds are ineffective in dynamic cloud environments, and simply consolidating virtual machines (VMs) to minimize energy costs does not necessarily result in the lowest carbon footprints. In this paper, a deep reinforcement learning (DRL) based framework called CFWS is proposed to enhance the energy efficiency of renewable energy sources (RES) supplied data centers (DCs). CFWS incorporates an adaptive thresholds adjustment method TCN-MAD by evaluating the predicted probability of a physical machine (PM) being overloaded to prevent unnecessary VM migrations and mitigate service level agreement (SLA) violations due to imbalanced workload distribution. Additionally, CFWS introduces a novel action space in the DRL algorithm by representing VM migrations among geo-distributed cloud data centers as flattened indices to accelerate its execution efficiency. Simulation results demonstrate that CFWS can achieve a superior optimization of energy costs and carbon footprints, saving 5.67% to 13.22% brown energy with maximized RES utilization. Furthermore, CFWS reduces VM migrations by up to 86.53% and maintains the lowest SLA violations within suboptimal execution time in comparison to the state-of-art algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"95-107"},"PeriodicalIF":3.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184034","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