Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate the importance of nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.
{"title":"ESPP: Efficient Sector-Based Charging Scheduling and Path Planning for WRSNs With Hexagonal Topology","authors":"Abdulbary Naji;Ammar Hawbani;Xingfu Wang;Haithm M. Al-Gunid;Yunes Al-Dhabi;Ahmed Al-Dubai;Amir Hussain;Liang Zhao;Saeed Hamood Alsamhi","doi":"10.1109/TSUSC.2023.3296607","DOIUrl":"10.1109/TSUSC.2023.3296607","url":null,"abstract":"Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate the \u0000<italic>importance</i>\u0000 of nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"31-45"},"PeriodicalIF":3.9,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79696001","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 : 2023-07-17DOI: 10.1109/TSUSC.2023.3295939
Sajib K. Biswas;Pranab K. Muhuri;Uttam K. Roy
Among the available processor-level energy savings schemes, dynamic voltage and frequency scaling (DVFS) is very popular and effective due to its widespread cross-platform use in designing energy-efficient scheduling algorithms. However, rapid frequency switching by DVFS based algorithms while minimizing the energy consumptions may result transient failures in the system. To avoid such failures and their catastrophic consequences, energy-efficient scheduling algorithms with the capabilities to provide more reliable task schedules are always in demand. Therefore, this paper introduces two novel low complexity energy-efficient task scheduling algorithms for heterogeneous computing environments. We term the first algorithm as ‘binary search-based energy-efficient scheduling with reliability goal (BSESRG)’ for running parallel task graphs in heterogeneous computing systems. We show that the proposed BSESRG has the capability to reduce energy consumption, and shorten the total schedule length by meeting the reliability goals upto a certain threshold. Then, we present our second algorithm, the ‘binary search-based energy-efficient fault-tolerant scheduling with reliability goal (BSESRG-FT), which ensures meeting the reliability goals with simultaneous consideration of fault tolerance. The proposed BSESRG-FT is able to reach higher reliability goals, reduce energy consumption, and shorten the total schedule length of a parallel task graph on heterogeneous platforms. We demonstrate the working of both BSESRG and BSESRG-FT through simulation experiments considering real-world task graphs, and show the supremacy of the two proposed algorithms over their respective peers (viz., ESRG and EFSRG) in terms of energy savings, schedule lengths, run times and reliability goals. The superiority of the proposed BSESRG and BSESRG-FT over their respective competitors are also validated on the real benchmark MiBench. Moreover, from the complexity analysis, we respectively find the time complexities of BSESRG and BSESRG-FT as $Omathbf {(|mathcal {X}|times |P| times log_{2}|F|)}$ and $Omathbf {(|mathcal {X}|times |P|^{2}times log_{2}|F|)}$ confirming their better computational efficiency than the respective peers.
{"title":"Binary Search-Based Fast Scheduling Algorithms for Reliability-Aware Energy-Efficient Task Graph Scheduling With Fault Tolerance","authors":"Sajib K. Biswas;Pranab K. Muhuri;Uttam K. Roy","doi":"10.1109/TSUSC.2023.3295939","DOIUrl":"10.1109/TSUSC.2023.3295939","url":null,"abstract":"Among the available processor-level energy savings schemes, dynamic voltage and frequency scaling (DVFS) is very popular and effective due to its widespread cross-platform use in designing energy-efficient scheduling algorithms. However, rapid frequency switching by DVFS based algorithms while minimizing the energy consumptions may result transient failures in the system. To avoid such failures and their catastrophic consequences, energy-efficient scheduling algorithms with the capabilities to provide more reliable task schedules are always in demand. Therefore, this paper introduces two novel low complexity energy-efficient task scheduling algorithms for heterogeneous computing environments. We term the first algorithm as ‘binary search-based energy-efficient scheduling with reliability goal (BSESRG)’ for running parallel task graphs in heterogeneous computing systems. We show that the proposed BSESRG has the capability to reduce energy consumption, and shorten the total schedule length by meeting the reliability goals upto a certain threshold. Then, we present our second algorithm, the ‘binary search-based energy-efficient fault-tolerant scheduling with reliability goal (BSESRG-FT), which ensures meeting the reliability goals with simultaneous consideration of fault tolerance. The proposed BSESRG-FT is able to reach higher reliability goals, reduce energy consumption, and shorten the total schedule length of a parallel task graph on heterogeneous platforms. We demonstrate the working of both BSESRG and BSESRG-FT through simulation experiments considering real-world task graphs, and show the supremacy of the two proposed algorithms over their respective peers (viz., ESRG and EFSRG) in terms of energy savings, schedule lengths, run times and reliability goals. The superiority of the proposed BSESRG and BSESRG-FT over their respective competitors are also validated on the real benchmark MiBench. Moreover, from the complexity analysis, we respectively find the time complexities of BSESRG and BSESRG-FT as \u0000<inline-formula><tex-math>$Omathbf {(|mathcal {X}|times |P| times log_{2}|F|)}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$Omathbf {(|mathcal {X}|times |P|^{2}times log_{2}|F|)}$</tex-math></inline-formula>\u0000 confirming their better computational efficiency than the respective peers.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"433-451"},"PeriodicalIF":3.9,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91127111","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 : 2023-07-11DOI: 10.1109/TSUSC.2023.3294447
Khalid M. Hosny;Ahmed I. Awad;Marwa M. Khashaba;Mostafa M. Fouda;Mohsen Guizani;Ehab R. Mohamed
Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined Whale Optimization Algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.
{"title":"Optimized Multi-User Dependent Tasks Offloading in Edge-Cloud Computing Using Refined Whale Optimization Algorithm","authors":"Khalid M. Hosny;Ahmed I. Awad;Marwa M. Khashaba;Mostafa M. Fouda;Mohsen Guizani;Ehab R. Mohamed","doi":"10.1109/TSUSC.2023.3294447","DOIUrl":"10.1109/TSUSC.2023.3294447","url":null,"abstract":"Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined Whale Optimization Algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"14-30"},"PeriodicalIF":3.9,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90507629","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 : 2023-07-07DOI: 10.1109/TSUSC.2023.3293269
Marco Anisetti;Claudio A. Ardagna;Alessandro Balestrucci;Nicola Bena;Ernesto Damiani;Chan Yeob Yeun
Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. Research on poisoning attacks and defenses received increasing attention in the last decade, leading to several promising solutions aiming to increase the robustness of machine learning. Among them, ensemble-based defenses, where different models are trained on portions of the training set and their predictions are then aggregated, provide strong theoretical guarantees at the price of a linear overhead. Surprisingly, ensemble-based defenses, which do not pose any restrictions on the base model, have not been applied to increase the robustness of random forest. The work in this paper aims to fill in this gap by designing and implementing a novel hash-based ensemble approach that protects random forest against untargeted, random poisoning attacks. An extensive experimental evaluation measures the performance of our approach against a variety of attacks, as well as its sustainability in terms of resource consumption and performance, and compares it with a traditional monolithic model based on random forest. A final discussion presents our main findings and compares our approach with existing poisoning defenses targeting random forests.
{"title":"On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach","authors":"Marco Anisetti;Claudio A. Ardagna;Alessandro Balestrucci;Nicola Bena;Ernesto Damiani;Chan Yeob Yeun","doi":"10.1109/TSUSC.2023.3293269","DOIUrl":"10.1109/TSUSC.2023.3293269","url":null,"abstract":"Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. Research on poisoning attacks and defenses received increasing attention in the last decade, leading to several promising solutions aiming to increase the robustness of machine learning. Among them, ensemble-based defenses, where different models are trained on portions of the training set and their predictions are then aggregated, provide strong theoretical guarantees at the price of a linear overhead. Surprisingly, ensemble-based defenses, which do not pose any restrictions on the base model, have not been applied to increase the robustness of random forest. The work in this paper aims to fill in this gap by designing and implementing a novel hash-based ensemble approach that protects random forest against untargeted, random poisoning attacks. An extensive experimental evaluation measures the performance of our approach against a variety of attacks, as well as its sustainability in terms of resource consumption and performance, and compares it with a traditional monolithic model based on random forest. A final discussion presents our main findings and compares our approach with existing poisoning defenses targeting random forests.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"540-554"},"PeriodicalIF":3.9,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90546537","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 : 2023-07-03DOI: 10.1109/TSUSC.2023.3291365
Meiyan Teng;Xin Li;Kun Zhu
Intelligent terminal devices (TDs) usually request delay-sensitive and resource-demanding jobs, which are consisted of many sequential tasks. Mobile edge computing (MEC) offloads tasks to edge networks closer to TDs, making up for the lack of long delay response in the cloud, but it has a limited energy supply. Thanks to low-energy TDs also having processing capacity, it is a critical and challenging issue to offload sequential tasks for sustainable computing and reducing carbon emission in a terminal-edge-cloud (TEC) architecture. Existing research on offloading is limited to MEC or cloud-edge coordination environment, and ignores the impact of sequential task ( S-Task ) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimal S-Task offloading and service deployment ( JOTOSD ) problems objected to maximize the energy utility related to response delay, which is NP-hard and is divided into deployment and offloading sub-problems. Then, we propose a comprehensive offloading and deployment ( COD ) method, including the Break-Point ( BP ) algorithm and the convex programming-based edge offloading ( CVEO ) algorithm under a service deployment strategy provided by an iterative service deployment ( ISD ) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.
{"title":"Joint Optimization of Sequential Task Offloading and Service Deployment in End-Edge-Cloud System for Energy Efficiency","authors":"Meiyan Teng;Xin Li;Kun Zhu","doi":"10.1109/TSUSC.2023.3291365","DOIUrl":"10.1109/TSUSC.2023.3291365","url":null,"abstract":"Intelligent terminal devices (TDs) usually request delay-sensitive and resource-demanding jobs, which are consisted of many sequential tasks. Mobile edge computing (MEC) offloads tasks to edge networks closer to TDs, making up for the lack of long delay response in the cloud, but it has a limited energy supply. Thanks to low-energy TDs also having processing capacity, it is a critical and challenging issue to offload sequential tasks for sustainable computing and reducing carbon emission in a \u0000<italic>terminal-edge-cloud</i>\u0000 (TEC) architecture. Existing research on offloading is limited to MEC or \u0000<italic>cloud-edge</i>\u0000 coordination environment, and ignores the impact of sequential task (\u0000<italic>S-Task</i>\u0000) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimal \u0000<italic>S-Task</i>\u0000 offloading and service deployment (\u0000<italic>JOTOSD</i>\u0000) problems objected to maximize the energy utility related to response delay, which is NP-hard and is divided into deployment and offloading sub-problems. Then, we propose a comprehensive offloading and deployment (\u0000<italic>COD</i>\u0000) method, including the Break-Point (\u0000<italic>BP</i>\u0000) algorithm and the convex programming-based edge offloading (\u0000<italic>CVEO</i>\u0000) algorithm under a service deployment strategy provided by an iterative service deployment (\u0000<italic>ISD</i>\u0000) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"283-298"},"PeriodicalIF":3.9,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77281807","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 : 2023-06-16DOI: 10.1109/TSUSC.2023.3286916
José González-Cabañas;Patricia Callejo;Rubén Cuevas;Steffen Svartberg;Tommy Torjesen;Ángel Cuevas;Antonio Pastor;Mikko Kotila
Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.
{"title":"CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online Ads","authors":"José González-Cabañas;Patricia Callejo;Rubén Cuevas;Steffen Svartberg;Tommy Torjesen;Ángel Cuevas;Antonio Pastor;Mikko Kotila","doi":"10.1109/TSUSC.2023.3286916","DOIUrl":"10.1109/TSUSC.2023.3286916","url":null,"abstract":"Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"739-750"},"PeriodicalIF":3.9,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74307122","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 : 2023-06-12DOI: 10.1109/TSUSC.2023.3284827
Zhuhuan Xu;Xin Guan;Haiyang Jiang;Yongnan Liu;Zhaogong Zhang;Hongyang Chen;Zhu Han
To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this article, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users’ load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.
{"title":"Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid","authors":"Zhuhuan Xu;Xin Guan;Haiyang Jiang;Yongnan Liu;Zhaogong Zhang;Hongyang Chen;Zhu Han","doi":"10.1109/TSUSC.2023.3284827","DOIUrl":"10.1109/TSUSC.2023.3284827","url":null,"abstract":"To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this article, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users’ load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"354-370"},"PeriodicalIF":3.9,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75378999","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 : 2023-06-08DOI: 10.1109/TSUSC.2023.3283518
Mir Ashraf Uddin;Man Lin;Laurence T. Yang
This work aims at addressing carbon neutrality challenges through resource management with system software control. Reducing energy costs is vital for modern systems, especially those battery-powered devices that need to perform complex tasks. The technique of dynamic voltage or frequency scaling (DVFS) has been commonly adopted for reducing power consumption in cyber-physical systems to support the increasing computation demand under limited battery life. Dynamic slack becomes available when a task finishes earlier than its worst execution time. Dynamic slack management is an important factor for the DVFS mechanism. This paper proposes a dynamic slack-sharing (DSS) DVFS scheduling method that reduces CPU energy consumption by learning the slack-sharing rate. The DSS method automatically changes the slack sharing rate of a task on the fly in different situations through learning from experience to determine how much slack to use for the next task and how much to share. The method used for learning is Q-learning. Extensive experiments have been performed, and the results show that the DSS technique achieves more energy savings than the existing ones.
这项工作旨在通过系统软件控制进行资源管理，应对碳中和挑战。降低能源成本对现代系统至关重要，尤其是那些需要执行复杂任务的电池供电设备。动态电压或频率缩放（DVFS）技术已被普遍用于降低网络物理系统的功耗，以支持在电池寿命有限的情况下不断增长的计算需求。当任务比其最坏执行时间提前完成时，就会出现动态松弛。动态松弛管理是 DVFS 机制的一个重要因素。本文提出了一种动态空闲共享（DSS）DVFS 调度方法，通过学习空闲共享率来降低 CPU 能耗。DSS 方法通过学习经验，在不同情况下自动改变任务的松弛共享率，以确定下一个任务使用多少松弛以及共享多少松弛。学习的方法是 Q-learning。我们进行了广泛的实验，结果表明 DSS 技术比现有技术节省了更多能源。
{"title":"Dynamic Slack-Sharing Learning Technique With DVFS for Real-Time Systems","authors":"Mir Ashraf Uddin;Man Lin;Laurence T. Yang","doi":"10.1109/TSUSC.2023.3283518","DOIUrl":"10.1109/TSUSC.2023.3283518","url":null,"abstract":"This work aims at addressing carbon neutrality challenges through resource management with system software control. Reducing energy costs is vital for modern systems, especially those battery-powered devices that need to perform complex tasks. The technique of dynamic voltage or frequency scaling (DVFS) has been commonly adopted for reducing power consumption in cyber-physical systems to support the increasing computation demand under limited battery life. Dynamic slack becomes available when a task finishes earlier than its worst execution time. Dynamic slack management is an important factor for the DVFS mechanism. This paper proposes a dynamic slack-sharing (DSS) DVFS scheduling method that reduces CPU energy consumption by learning the slack-sharing rate. The DSS method automatically changes the slack sharing rate of a task on the fly in different situations through learning from experience to determine how much slack to use for the next task and how much to share. The method used for learning is Q-learning. Extensive experiments have been performed, and the results show that the DSS technique achieves more energy savings than the existing ones.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"261-270"},"PeriodicalIF":3.9,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83161277","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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