Pub Date : 2025-03-07DOI: 10.1109/TSUSC.2025.3549001
Jie Li;Yuhui Deng;Zijie Zhong;Zhaorui Wu;Shujie Pang;Lin Cui;Geyong Min
The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed EAVMS- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23$ %$–43.07$ %$ in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random).
{"title":"An Energy-Aware Virtual Machine Scheduling Approach for Cloud Data Centers","authors":"Jie Li;Yuhui Deng;Zijie Zhong;Zhaorui Wu;Shujie Pang;Lin Cui;Geyong Min","doi":"10.1109/TSUSC.2025.3549001","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549001","url":null,"abstract":"The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed <i>EAVMS</i>- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23<inline-formula><tex-math>$ %$</tex-math></inline-formula>–43.07<inline-formula><tex-math>$ %$</tex-math></inline-formula> in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random).","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"891-907"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248077","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 : 2025-03-07DOI: 10.1109/TSUSC.2025.3549112
Muhammad Ameen;Pengfei Wang;Weijian Su;Xiaopeng Wei;Qiang Zhang
Federated unlearning (FUL) is a solution aimed at addressing the problem of removing data contributions from trained federated learning (FL) models. Existing FUL methods only focus on iterative unlearning of clients’ contributions and fail to perform unlearning in scenarios where multiple clients request to remove their data at a time. Additionally, FUL still needs to address issues, including convergence speed, maintaining the global model’s performance, and parallel unlearning to expedite the unlearning process. To fill this gap, we introduce Federated Clients Forgetting (FedCF), a fast and accurate FUL method that can eliminate single client contributions similar to existing methods, eliminate multiple clients’ contributions on the global model parallelly, ensure the performance of the unlearned global model, and reduce the unlearning time. The key idea is to construct a temporary model by extracting knowledge from the remaining clients’ updates and adding it to the corresponding parameters of the initial global model and then leverage a temporary model to reconstruct the unlearned global model. Extensive experiments on three benchmark datasets, FedCF demonstrates its efficiency and effectiveness for single client contribution unlearning, achieving an average time efficiency of 8.3x, 6.5x, and 4.1x over existing methods FedRetrain, FedEraser, and FUL with knowledge distillation, respectively. Additionally, FedCF showcases the time efficiency and performance guarantee after unlearning the contributions of multiple clients in parallel.
{"title":"Speed up Federated Unlearning With Temporary Local Models","authors":"Muhammad Ameen;Pengfei Wang;Weijian Su;Xiaopeng Wei;Qiang Zhang","doi":"10.1109/TSUSC.2025.3549112","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549112","url":null,"abstract":"Federated unlearning (FUL) is a solution aimed at addressing the problem of removing data contributions from trained federated learning (FL) models. Existing FUL methods only focus on iterative unlearning of clients’ contributions and fail to perform unlearning in scenarios where multiple clients request to remove their data at a time. Additionally, FUL still needs to address issues, including convergence speed, maintaining the global model’s performance, and parallel unlearning to expedite the unlearning process. To fill this gap, we introduce Federated Clients Forgetting (FedCF), a fast and accurate FUL method that can eliminate single client contributions similar to existing methods, eliminate multiple clients’ contributions on the global model parallelly, ensure the performance of the unlearned global model, and reduce the unlearning time. The key idea is to construct a temporary model by extracting knowledge from the remaining clients’ updates and adding it to the corresponding parameters of the initial global model and then leverage a temporary model to reconstruct the unlearned global model. Extensive experiments on three benchmark datasets, FedCF demonstrates its efficiency and effectiveness for single client contribution unlearning, achieving an average time efficiency of 8.3x, 6.5x, and 4.1x over existing methods FedRetrain, FedEraser, and FUL with knowledge distillation, respectively. Additionally, FedCF showcases the time efficiency and performance guarantee after unlearning the contributions of multiple clients in parallel.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"921-936"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248089","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}
Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird’s eye view (BEV) of cameras. Cross-illumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based low-carbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semantics-aware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7%, and 76.6%, respectively.
{"title":"Semantic Communication-Based Low-Carbon Sustainable Framework for Person Re-Identification","authors":"Hao Liu;Wenhan Long;Xinlong Wen;Zhida Guo;Lu Liu;Rongbo Zhu","doi":"10.1109/TSUSC.2025.3566622","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3566622","url":null,"abstract":"Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird’s eye view (BEV) of cameras. Cross-illumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based low-carbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semantics-aware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7%, and 76.6%, respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"982-992"},"PeriodicalIF":3.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248097","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 : 2025-03-01DOI: 10.1109/TSUSC.2025.3566072
Liping Tao;Yang Lu;Yuqi Fan;Lei Shi;Zhen Wei
Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.
{"title":"Storage Scalability Oriented Segment Allocation Based on Cost Clustering in Sharding Blockchains","authors":"Liping Tao;Yang Lu;Yuqi Fan;Lei Shi;Zhen Wei","doi":"10.1109/TSUSC.2025.3566072","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3566072","url":null,"abstract":"Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"993-1006"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248066","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 : 2025-02-21DOI: 10.1109/TSUSC.2025.3544430
Mohammed Alswaitti;Roberto Verdecchia;Grégoire Danoy;Pascal Bouvry;Johnatan E. Pecero
The substantial increase in AI model training has considerable environmental implications, requiring energy-efficient and sustainable AI practices. On one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models’ performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice. In essence, this study establishes a new benchmark for AI researchers and practitioners interested in improving the environmental sustainability of AI model training via data-centric approaches.
{"title":"Training Green AI Models Using Elite Samples","authors":"Mohammed Alswaitti;Roberto Verdecchia;Grégoire Danoy;Pascal Bouvry;Johnatan E. Pecero","doi":"10.1109/TSUSC.2025.3544430","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3544430","url":null,"abstract":"The substantial increase in AI model training has considerable environmental implications, requiring energy-efficient and sustainable AI practices. On one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models’ performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice. In essence, this study establishes a new benchmark for AI researchers and practitioners interested in improving the environmental sustainability of AI model training via data-centric approaches.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"858-872"},"PeriodicalIF":3.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248038","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 : 2025-02-21DOI: 10.1109/TSUSC.2025.3544249
Yan Jiang;Kun Xie;Yudian Ouyang;Jigang Wen;Guangxing Zhang;Wei Liang;Quan Feng
Remote sensing imagery plays a crucial role in areas such as environmental monitoring and urban planning. However, due to fragile communication links, limited bandwidth and harsh wireless environments, transmitting data from remote locations to ground applications faces the dilemma of high bit-error rates, which have a poor impact on downstream missions. Semantic communication is a feasible solution that transmits only the semantic features of the raw data extracted using neural networks. Although effective, existing semantic communication methods cannot cope with high compression rate requirements and complex communication environments. Therefore, in this paper, an effective image compression and transmission framework ASE-JSCC is proposed. To minimize the transmitted data, we design a semantic extraction module and an important feature selection module to efficiently extract, select, and compress critical semantic features required for downstream tasks. To improve the communication robustness of the model in complex environments affected by variable channels, we optimize the source-channel joint coding technique by randomly adding noise with different types and sizes. Finally, we deploy ASE-JSCC to the scene classification task of remote sensing images and conduct extensive experiments on four real datasets, achieving classification accuracy of 84.29%--88.62% under 384 times compression ratio, verifying the excellent performance of the proposed framework.
{"title":"High Quality Compression and Transmission of Remote Sensing Images Based on Semantic Communication","authors":"Yan Jiang;Kun Xie;Yudian Ouyang;Jigang Wen;Guangxing Zhang;Wei Liang;Quan Feng","doi":"10.1109/TSUSC.2025.3544249","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3544249","url":null,"abstract":"Remote sensing imagery plays a crucial role in areas such as environmental monitoring and urban planning. However, due to fragile communication links, limited bandwidth and harsh wireless environments, transmitting data from remote locations to ground applications faces the dilemma of high bit-error rates, which have a poor impact on downstream missions. Semantic communication is a feasible solution that transmits only the semantic features of the raw data extracted using neural networks. Although effective, existing semantic communication methods cannot cope with high compression rate requirements and complex communication environments. Therefore, in this paper, an effective image compression and transmission framework ASE-JSCC is proposed. To minimize the transmitted data, we design a semantic extraction module and an important feature selection module to efficiently extract, select, and compress critical semantic features required for downstream tasks. To improve the communication robustness of the model in complex environments affected by variable channels, we optimize the source-channel joint coding technique by randomly adding noise with different types and sizes. Finally, we deploy ASE-JSCC to the scene classification task of remote sensing images and conduct extensive experiments on four real datasets, achieving classification accuracy of 84.29%--88.62% under 384 times compression ratio, verifying the excellent performance of the proposed framework.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"843-857"},"PeriodicalIF":3.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248044","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 : 2025-02-17DOI: 10.1109/TSUSC.2025.3542466
Dun Li;Hongzhi Li;Noel Crespi;Roberto Minerva;Ming Li;Wei Liang;Kuan-Ching Li
In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.
{"title":"Hyper-IIoT: A Smart Contract-Inspired Access Control Scheme for Resource-Constrained Industrial Internet of Things","authors":"Dun Li;Hongzhi Li;Noel Crespi;Roberto Minerva;Ming Li;Wei Liang;Kuan-Ching Li","doi":"10.1109/TSUSC.2025.3542466","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3542466","url":null,"abstract":"In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"820-829"},"PeriodicalIF":3.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248042","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 : 2025-02-13DOI: 10.1109/TSUSC.2025.3541958
Ming Ling;Chuanzhao Zhang;Shidi Tang;Ruiqi Chen;Yanxiang Zhu
Virtual screening plays an indispensable role in the early stages of drug discovery, which utilizes high-throughput molecular docking to find potential drug candidates from vast databases. Virtual screening necessitates considerable computational resources to analyze tremendous compounds. However, the substantial demand for computational resources and the challenges in accessing high performance hardware hinders the development of drug discovery. This work introduces EEVS (Economic and Ecological Virtual Screening), an innovative framework that utilizes the computational capabilities of discarded smartphones for cost-effective and eco-friendly virtual screening. EEVS, with 16 discarded smartphones in this study, greatly reduces the construction cost of virtual screening, which is only 38.7%, 11.9%, and 26.9% of those of CPU, GPU, and FPGA implementations, respectively. Moreover, EEVS achieves a 4.05× improvement in screening speed while maintaining similar power and docking accuracy with CPU. When compared with GPU and FPGA, EEVS attains advantages of 4.93× in screening power and 1.08× in screening speed, respectively. Furthermore, we proposed the PCSA algorithm to further accelerate the screening speed of EEVS by a maximum of 33.6% while balancing various thermal dissipation requirements. To the best of our knowledge, this work is the first virtual screening framework that leverages discarded smartphones to accelerate drug discovery.
{"title":"EEVS: Redeploying Discarded Smartphones for Economic and Ecological Drug Molecules Virtual Screening","authors":"Ming Ling;Chuanzhao Zhang;Shidi Tang;Ruiqi Chen;Yanxiang Zhu","doi":"10.1109/TSUSC.2025.3541958","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3541958","url":null,"abstract":"Virtual screening plays an indispensable role in the early stages of drug discovery, which utilizes high-throughput molecular docking to find potential drug candidates from vast databases. Virtual screening necessitates considerable computational resources to analyze tremendous compounds. However, the substantial demand for computational resources and the challenges in accessing high performance hardware hinders the development of drug discovery. This work introduces EEVS (Economic and Ecological Virtual Screening), an innovative framework that utilizes the computational capabilities of discarded smartphones for cost-effective and eco-friendly virtual screening. EEVS, with 16 discarded smartphones in this study, greatly reduces the construction cost of virtual screening, which is only 38.7%, 11.9%, and 26.9% of those of CPU, GPU, and FPGA implementations, respectively. Moreover, EEVS achieves a 4.05× improvement in screening speed while maintaining similar power and docking accuracy with CPU. When compared with GPU and FPGA, EEVS attains advantages of 4.93× in screening power and 1.08× in screening speed, respectively. Furthermore, we proposed the PCSA algorithm to further accelerate the screening speed of EEVS by a maximum of 33.6% while balancing various thermal dissipation requirements. To the best of our knowledge, this work is the first virtual screening framework that leverages discarded smartphones to accelerate drug discovery.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"830-842"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248063","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 : 2024-12-11DOI: 10.1109/TSUSC.2024.3415951
Zhongwen Guo;Hui Xia;Yu Wang;Radhouane Chouchane
{"title":"Guest Editorial of the Special Section on AI Powered Edge Computing for IoT","authors":"Zhongwen Guo;Hui Xia;Yu Wang;Radhouane Chouchane","doi":"10.1109/TSUSC.2024.3415951","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3415951","url":null,"abstract":"","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"814-816"},"PeriodicalIF":3.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810500","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：