IEEE Transactions on Sustainable Computing最新文献_第8页

Automatic Software Tailoring for Optimal Performance 自动定制软件，实现最佳性能

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-11-06 DOI: 10.1109/TSUSC.2023.3330671

José Miguel Aragón-Jurado;Juan Carlos de la Torre;Patricia Ruiz;Pedro L. Galindo;Albert Y. Zomaya;Bernabé Dorronsoro

{"title":"Automatic Software Tailoring for Optimal Performance","authors":"José Miguel Aragón-Jurado;Juan Carlos de la Torre;Patricia Ruiz;Pedro L. Galindo;Albert Y. Zomaya;Bernabé Dorronsoro","doi":"10.1109/TSUSC.2023.3330671","DOIUrl":"10.1109/TSUSC.2023.3330671","url":null,"abstract":"Efficient green software solutions require being aware of the characteristics of both the software and the hardware where it is executed. Separately optimizing them leads to inefficient results, and there is a need for a perfect synergy between software and hardware for optimal outcomes. We present a novel combinatorial optimization problem for the minimization of the software execution time on a specific hardware, taking into account the existing uncertainty in the system. A solution to the problem is a sequence of LLVM code transformations, and a cellular genetic algorithm is used to find it. Assuming that hardware does not change, reducing the software runtime typically leads to a greener version with lower consumption. To cope with the uncertainty, two novel approaches relying on bootstrap method to compute confident intervals of the software runtime at negligible cost are proposed and compared to three other techniques and −O3 Clang compilation flag over four hardware architectures. Results show how the proposed approach effectively copes with the uncertainty, providing more robust solutions with respect to the compared methods. The execution time of the raw program is reduced from 28.1% to up to 63.2%, outperforming −O3 flag by 13.9% to 26.3%, for the different architectures.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"464-481"},"PeriodicalIF":3.9,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501605","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

Parallel Trajectory Training of Recurrent Neural Network Controllers With Levenberg–Marquardt and Forward Accumulation Through Time in Closed-Loop Control Systems 在闭环控制系统中利用 Levenberg-Marquardt 和时间前向累积对递归神经网络控制器进行并行轨迹训练

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-11-06 DOI: 10.1109/TSUSC.2023.3330573

Xingang Fu;Jordan Sturtz;Eduardo Alonso;Rajab Challoo;Letu Qingge

{"title":"Parallel Trajectory Training of Recurrent Neural Network Controllers With Levenberg–Marquardt and Forward Accumulation Through Time in Closed-Loop Control Systems","authors":"Xingang Fu;Jordan Sturtz;Eduardo Alonso;Rajab Challoo;Letu Qingge","doi":"10.1109/TSUSC.2023.3330573","DOIUrl":"10.1109/TSUSC.2023.3330573","url":null,"abstract":"This paper introduces a novel parallel trajectory mechanism that combines Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train a recurrent neural network controller in a closed-loop control system by distributing the calculation of trajectories across Central Processing Unit (CPU) cores/workers depending on the computing platforms, computing program languages, and software packages available. Without loss of generality, the recurrent neural network controller of a grid-connected converter for solar integration to a power system was selected as the benchmark test closed-loop control system. Two software packages were developed in Matlab and C++ to verify and demonstrate the efficiency of the proposed parallel training method. The training of the deep neural network controller was migrated from a single workstation to both cloud computing platforms and High-Performance Computing clusters. The training results show excellent speed-up performance, which significantly reduces the training time for a large number of trajectories with high sampling frequency, and further demonstrates the effectiveness and scalability of the proposed parallel mechanism.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"222-229"},"PeriodicalIF":3.9,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501371","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

Shortest Paths Publishing With Differential Privacy 利用差分隐私发布最短路径

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-11-03 DOI: 10.1109/TSUSC.2023.3329995

Bin Cai;Weihong Sheng;Jiajun Chen;Chunqiang Hu;Jiguo Yu

{"title":"Shortest Paths Publishing With Differential Privacy","authors":"Bin Cai;Weihong Sheng;Jiajun Chen;Chunqiang Hu;Jiguo Yu","doi":"10.1109/TSUSC.2023.3329995","DOIUrl":"10.1109/TSUSC.2023.3329995","url":null,"abstract":"The growing prevalence of graphs representations in our society has led to a corresponding rise in the publishing of graphs by researchers and organizations. To protect the privacy, it is important to ensure that graphs including sensitive data are not disclosed. Since the weight of edges could be utilized to infer confidential information, the graph should be privately published to avoid ethical and legal issues. In this paper, we propose a novel method for privately publishing shortest paths while preserving the privacy of sensitive edge weights in graph. Specifically, we divide the edge weights into internal and external edges based on their edge betweenness centrality. Then, we give two different differentially private algorithms to perturb edge weights based on the distinction between internal and external edges, respectively. To reduce the error ratios between differentially private shortest paths and real shortest paths, we employ edge betweenness centrality to search for the shortest path, which is closest to the true one. Our experimental results show that our mechanisms can effectively reduce the error in the average shortest path distance by 1.1% for large graphs, while for the shortest path change rate, our mechanisms can reduce it by 8.3%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"209-221"},"PeriodicalIF":3.9,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134982272","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

Self-Optimizing the Environmental Sustainability of Blockchain-Based Systems 区块链系统的环境可持续性自我优化

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-10-19 DOI: 10.1109/TSUSC.2023.3325881

Akram Alofi;Mahmoud A. Bokhari;Rami Bahsoon;Robert Hendley

{"title":"Self-Optimizing the Environmental Sustainability of Blockchain-Based Systems","authors":"Akram Alofi;Mahmoud A. Bokhari;Rami Bahsoon;Robert Hendley","doi":"10.1109/TSUSC.2023.3325881","DOIUrl":"10.1109/TSUSC.2023.3325881","url":null,"abstract":"Blockchain technology has been widely adopted in many areas to provide more dependable and trustworthy systems, including digital infrastructure. Nevertheless, its widespread implementation is accompanied by significant environmental concerns, as it is considered a substantial contributor to greenhouse gas emissions. This environmental impact is mainly attributed to the inherent inefficiencies of its consensus algorithms, notably Proof of Work, which demands substantial computational power for trust establishment. This paper proposes a novel self-adaptive model to optimize the environmental sustainability of blockchain-based systems, addressing energy consumption and carbon emission without compromising the fundamental properties of blockchain technology. The model continuously monitors a blockchain-based system and adaptively selects miners, considering context changes and user needs. It dynamically selects a subset of miners to perform sustainable mining processes while ensuring the decentralization and trustworthiness of the system. The aim is to minimize blockchain-based systems' energy consumption and carbon emissions while maximizing their decentralization and trustworthiness. We conduct experiments to evaluate the efficiency and effectiveness of the model. The results show that our self-optimizing model can reduce energy consumption by 55.49% and carbon emissions by 71.25% on average while maintaining desirable levels of decentralization and trustworthiness by more than 96.08% and 75.12%, respectively. Furthermore, these enhancements can be achieved under different operating conditions compared to similar models, including the straightforward use of Proof of Work. Also, we have investigated and discussed the correlation between these objectives and how they are related to the number of miners within the blockchain-based systems.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"396-408"},"PeriodicalIF":3.9,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135058244","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

Multi-Type Charging Scheduling Based on Area Requirement Difference for Wireless Rechargeable Sensor Networks 基于区域需求差异的无线充电传感器网络多类型充电调度

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-10-17 DOI: 10.1109/TSUSC.2023.3325237

Yang Yang;Xuxun Liu;Kun Tang;Wenquan Che;Quan Xue

{"title":"Multi-Type Charging Scheduling Based on Area Requirement Difference for Wireless Rechargeable Sensor Networks","authors":"Yang Yang;Xuxun Liu;Kun Tang;Wenquan Che;Quan Xue","doi":"10.1109/TSUSC.2023.3325237","DOIUrl":"10.1109/TSUSC.2023.3325237","url":null,"abstract":"Charging scheduling plays a crucial role in ensuring durable operation for wireless rechargeable sensor networks. However, previous methods cannot meet the strict requirements of a high node survival rate and high energy usage effectiveness. In this article, we propose a multi-type charging scheduling strategy to meet such demands. In this strategy, the network is divided into an inner ring and an outer ring to satisfy different demands in different areas. The inner ring forms a flat topology, and adopts a periodic and single-node charging pattern mainly for a high node survival rate. A space priority and a time priority are designed to determine the charging sequence of the nodes. The optimal charging cycle and the optimal charging time are achieved by mathematical derivations. The outer ring forms a cluster topology, and adopts an on-demand and multi-node charging pattern mainly for high energy usage effectiveness. A space balancing principle and a time balancing principle are designed to determine the charging positions of the clusters. A gravitational search algorithm is designed to determine the charging sequence of the clusters. Several simulations verify the advantages of the proposed solution in terms of energy usage effectiveness, charging failure rate, and average task delay.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"182-196"},"PeriodicalIF":3.9,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135007433","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

FPGA Implementation of Classical Dynamic Neural Networks for Smooth and Nonsmooth Optimization Problems 针对平滑和非平滑优化问题的经典动态神经网络的 FPGA 实现

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-10-17 DOI: 10.1109/TSUSC.2023.3325268

Renfeng Xiao;Xing He;Tingwen Huang;Junzhi Yu

引用次数: 0

DNN-SNN Co-Learning for Sustainable Symbol Detection in 5G Systems on Loihi Chip 利用 DNN-SNN 协同学习实现 Loihi 芯片 5G 系统中的可持续符号检测

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-10-13 DOI: 10.1109/TSUSC.2023.3324339

Shiya Liu;Yibin Liang;Yang Yi

{"title":"DNN-SNN Co-Learning for Sustainable Symbol Detection in 5G Systems on Loihi Chip","authors":"Shiya Liu;Yibin Liang;Yang Yi","doi":"10.1109/TSUSC.2023.3324339","DOIUrl":"10.1109/TSUSC.2023.3324339","url":null,"abstract":"Performing symbol detection for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is challenging and resource-consuming. In this paper, we present a liquid state machine (LSM), a type of reservoir computing based on spiking neural networks (SNNs), to achieve energy-efficient and sustainable symbol detection on the Loihi chip for MIMO-OFDM systems. SNNs are more biological-plausible and energy-efficient than conventional deep neural networks (DNN) but have lower performance in terms of accuracy. To enhance the accuracy of SNNs, we propose a knowledge distillation training algorithm called DNN-SNN co-learning, which employs a bi-directional learning path between a DNN and an SNN. Specifically, the knowledge from the output and intermediate layer of the DNN is transferred to the SNN, and we exploit a decoder to convert the spikes in the intermediate layers of an SNN into real numbers to enable communication between the DNN and the SNN. Through the bi-directional learning path, the SNN can mimic the behavior of the DNN by learning the knowledge from the DNN. Conversely, the DNN can better adapt itself to the SNN by using the knowledge from the SNN. We introduce a new loss function to enable knowledge distillation on regression tasks. Our LSM is implemented on Intel's Loihi neuromorphic chip, a specialized hardware platform for SNN models. The experimental results on symbol detection in MIMO-OFDM systems demonstrate that our LSM on the Loihi chip is more precise than conventional symbol detection algorithms. Also, the model consumes approximately 6 times less energy per sample than other quantized DNN-based models with comparable accuracy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"170-181"},"PeriodicalIF":3.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136303279","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

Model-Free GPU Online Energy Optimization 无模型 GPU 在线能源优化

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-09-13 DOI: 10.1109/TSUSC.2023.3314916

Farui Wang;Meng Hao;Weizhe Zhang;Zheng Wang

{"title":"Model-Free GPU Online Energy Optimization","authors":"Farui Wang;Meng Hao;Weizhe Zhang;Zheng Wang","doi":"10.1109/TSUSC.2023.3314916","DOIUrl":"10.1109/TSUSC.2023.3314916","url":null,"abstract":"GPUs play a central and indispensable role as accelerators in modern high-performance computing (HPC) platforms, enabling a wide range of tasks to be performed efficiently. However, the use of GPUs also results in significant energy consumption and carbon dioxide (CO2) emissions. This article presents MF-GPOEO, a model-free GPU online energy efficiency optimization framework. MF-GPOEO leverages a synthetic performance index and a PID controller to dynamically determine the optimal clock frequency configuration for GPUs. It profiles GPU kernel activity information under different frequency configurations and then compares GPU kernel execution time and gap duration between kernels to derive the synthetic performance index. With the performance index and measured average power, MF-GPOEO can use the PID controller to try different frequency configurations and find the optimal frequency configuration under the guidance of user-defined objective functions. We evaluate the MF-GPOEO by running it with 74 applications on an NVIDIA RTX3080Ti GPU. MF-GPOEO delivers a mean energy saving of 26.2% with a slight average execution time increase of 3.4% compared with NVIDIA's default clock scheduling strategy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"141-154"},"PeriodicalIF":3.9,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402321","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

Reliability Enhancement Strategies for Workflow Scheduling Under Energy Consumption Constraints in Clouds 云中能耗约束下工作流调度的可靠性增强策略

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-09-12 DOI: 10.1109/TSUSC.2023.3314759

Longxin Zhang;Minghui Ai;Ke Liu;Jianguo Chen;Kenli Li

{"title":"Reliability Enhancement Strategies for Workflow Scheduling Under Energy Consumption Constraints in Clouds","authors":"Longxin Zhang;Minghui Ai;Ke Liu;Jianguo Chen;Kenli Li","doi":"10.1109/TSUSC.2023.3314759","DOIUrl":"10.1109/TSUSC.2023.3314759","url":null,"abstract":"As the demand for Big Data analysis and artificial intelligence technology continues to surge, a significant amount of research has been conducted on cloud computing services. An effective workflow scheduling strategy stands as the pivotal factor in ensuring the quality of cloud services. Dynamic voltage and frequency scaling (DVFS) is an effective energy-saving technology that is extensively used in the development of workflow scheduling algorithms. However, DVFS reduces the processor's running frequency, which increases the possibility of soft errors in workflow execution, thereby lowering the workflow execution reliability. This study proposes an energy-aware reliability enhancement scheduling (EARES) method with a checkpoint mechanism to improve system reliability while meeting the workflow deadline and the energy consumption constraints. The proposed EARES algorithm consists of three phases, namely, workflow application initialization, deadline partitioning, and energy partitioning and virtual machine selection. Numerous experiments are conducted to assess the performance of the EARES algorithm using three real-world scientific workflows. Experimental results demonstrate that the EARES algorithm remarkably improves reliability in comparison with other state-of-the-art algorithms while meeting the deadline and satisfying the energy consumption requirement.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"155-169"},"PeriodicalIF":3.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135401489","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

Computation Offloading for Energy Efficiency Maximization of Sustainable Energy Supply Network in IIoT 为实现物联网中可持续能源供应网络的能效最大化而进行计算卸载

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-09-11 DOI: 10.1109/TSUSC.2023.3313770

Zhao Tong;Jinhui Cai;Jing Mei;Kenli Li;Keqin Li

{"title":"Computation Offloading for Energy Efficiency Maximization of Sustainable Energy Supply Network in IIoT","authors":"Zhao Tong;Jinhui Cai;Jing Mei;Kenli Li;Keqin Li","doi":"10.1109/TSUSC.2023.3313770","DOIUrl":"10.1109/TSUSC.2023.3313770","url":null,"abstract":"The efficiency of production and equipment maintenance costs in the Industrial Internet of Things (IIoT) are directly impacted by equipment lifetime, making it an important concern. Mobile edge computing (MEC) can enhance network performance, extend device lifetime, and effectively reduce carbon emissions by integrating energy harvesting (EH) technology. However, when the two are combined, the coupling effect of energy and the system's communication resource management pose a great challenge to the development of computational offloading strategies. This paper investigates the problem of maximizing the energy efficiency of computation offloading in a two-tier MEC network powered by wireless power transfer (WPT). First, the corresponding mathematical models are developed for local computing, edge server processing, communication, and EH. The proposed fractional problem is transformed into a stochastic optimization problem by Dinkelbach method. In addition, virtual power queues are introduced to eliminate energy coupling effects by maintaining the stability of the battery power queues. Next, the problem is then resolved through the utilization of both Lyapunov optimization and convex optimization method. Consequently, a wireless energy transmission-based algorithm for maximizing energy efficiency is proposed. Finally, energy efficiency, an important parameter of network performance, is used as an indicator. The excellent performance of the EEMA-WET algorithm is verified through extensive extension and comparison experiments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 2","pages":"128-140"},"PeriodicalIF":3.9,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135361324","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