Pub Date : 2024-06-06DOI: 10.1109/TSUSC.2024.3410237
Muhammad Rizwan;Mudassir Ali;Ammar Hawbani;Wang Xingfu;Adeel Anjum;Pelin Angin;Olaoluwa Popoola;Muhammad Ali Imran
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.
{"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}
Pub Date : 2024-06-05DOI: 10.1109/TSUSC.2024.3409712
Mingshu He;Xiaowei Zhao;Xiaojuan Wang
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$%$ accuracy on the CICIDS2017 offline dataset and demonstrates a latency of only 1.05 seconds with a 99.86$%$ accuracy in online testing, surpassing other methods in terms of response speed.
{"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}
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.
{"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}
Pub Date : 2024-06-03DOI: 10.1109/TSUSC.2024.3408630
Prajnyajit Mohanty;Umesh C. Pati;Kamalakanta Mahapatra;Saraju P. Mohanty
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.
{"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}
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.
{"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}
Pub Date : 2024-04-24DOI: 10.1109/TSUSC.2024.3393357
Smruti Rekha Swain;Deepika Saxena;Jatinder Kumar;Ashutosh Kumar Singh;Chung-Nan Lee
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%.
{"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}
Pub Date : 2024-04-22DOI: 10.1109/TSUSC.2024.3392480
Wenkai Guan;Cristinel Ababei
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.
能效是计算领域最重要的挑战之一。处理器性能的提升速度与能效的低提升速度之间的差距越来越大。本文回答了如何降低异构数据中心能耗的问题。它提出了一种使用 D 选择技术的统一分层调度方法，该方法考虑了干扰和异构性。异构性来自服务器的不断升级，以及集成的高性能 "大 "内核和高能效 "小 "内核。这导致数据中心变得越来越异构，传统的作业调度算法也变得不理想。为此，我们为数据中心提出了一种两级分层调度器，以利用不断增加的服务器异构性。它以统一的方式结合了集群级和节点级调度算法，并可考虑特定的优化目标，包括作业完成时间、能源使用和能耗延迟积（EDP）。它的新颖之处在于采用了统一方法，并对干扰和异构性进行了建模。在一个研究集群上进行的实验发现，所提出的方法在作业完成时间、能源使用和能耗延迟积（EDP）方面分别比最先进的调度器优胜约 10%、39% 和 42%。本文证明了统一方法是优化异构数据中心能源和性能的一个有前途的方向。
{"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}
Pub Date : 2024-04-22DOI: 10.1109/TSUSC.2024.3391791
Daming Zhao;Jian-tao Zhou;Keqin Li
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.
{"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}
Pub Date : 2024-04-19DOI: 10.1109/TSUSC.2024.3391733
Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao
This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the $F$ -radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.
{"title":"Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach","authors":"Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao","doi":"10.1109/TSUSC.2024.3391733","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391733","url":null,"abstract":"This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the \u0000<inline-formula><tex-math>$F$</tex-math></inline-formula>\u0000-radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"803-813"},"PeriodicalIF":3.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397226","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}
Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.
{"title":"Adaptive Mobile Chargers Scheduling Scheme Based on AHP-MCDM for WRSN","authors":"Kondwani Makanda;Ammar Hawbani;Xingfu Wang;Abdulbary Naji;Ahmed Al-Dubai;Liang Zhao;Saeed Hamood Alsamhi","doi":"10.1109/TSUSC.2024.3391316","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391316","url":null,"abstract":"Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"57-69"},"PeriodicalIF":3.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184027","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}
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