Pub Date : 2024-03-17DOI: 10.1109/TSUSC.2024.3401003
Kangshun Li;Shumin Xie;Tianjin Zhu;Hui Wang
With the development of technology, unmanned aerial vehicles (UAVs) and Internet of Things devices are widely used in smart agriculture, resulting in significant energy consumption. In this paper, the optimization problem for UAV-assisted mobile computing in smart agriculture is modeled as a constrained multiobjective optimization problem. By jointly optimizing the deployment position of UAVs, the offloading location of the tasks, the transmit power of the devices, and the resource allocation of the UAVs, two optimization objectives (total delay and energy consumption) are minimized simultaneously. In view of the complex constraints, a constrained multiobjective algorithm named JO-DPTS is proposed. The algorithm adopts dual-population and two-stage approach to improve population convergence and diversity. The simulation results substantiate that JO-DPTS exhibits superior performance compared to the other three state-of-the-art constrained multiobjective evolutionary algorithms.
{"title":"Constrained Multiobjective Optimization for UAV-Assisted Mobile Edge Computing in Smart Agriculture: Minimizing Delay and Energy Consumption","authors":"Kangshun Li;Shumin Xie;Tianjin Zhu;Hui Wang","doi":"10.1109/TSUSC.2024.3401003","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3401003","url":null,"abstract":"With the development of technology, unmanned aerial vehicles (UAVs) and Internet of Things devices are widely used in smart agriculture, resulting in significant energy consumption. In this paper, the optimization problem for UAV-assisted mobile computing in smart agriculture is modeled as a constrained multiobjective optimization problem. By jointly optimizing the deployment position of UAVs, the offloading location of the tasks, the transmit power of the devices, and the resource allocation of the UAVs, two optimization objectives (total delay and energy consumption) are minimized simultaneously. In view of the complex constraints, a constrained multiobjective algorithm named JO-DPTS is proposed. The algorithm adopts dual-population and two-stage approach to improve population convergence and diversity. The simulation results substantiate that JO-DPTS exhibits superior performance compared to the other three state-of-the-art constrained multiobjective evolutionary algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"948-957"},"PeriodicalIF":3.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810513","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 : 2024-03-08DOI: 10.1109/TSUSC.2024.3371544
Ke Yuan;Peng Sang;Jian Ge;Bingcai Zhou;Chunfu Jia
E-Voting is widely used in many social, economic, political and cultural fields for its convenience, efficiency and greenness, but how to guarantee the fairness of e-voting and the controllability of human intervention needs further in-depth research and exploration. Although the introduction of homomorphic encryption algorithm solves the problem of ballot privacy calculation, and most of these schemes solve the problem of private key confidentiality by using or overlaying multiple different methods of saving private keys, its security will be questioned as long as there is a possibility of human intervention in the saving process. To solve this problem, we propose a timed-release e-voting scheme based on Paillier homomorphic encryption. We analyze the semantic security of the ballot formally by defining the security game, and realize the legitimacy check of the ballot ciphertext through the idea of partial knowledge proof. Property analysis shows that this scheme satisfies the basic properties of the security requirements of the e-voting scheme. Performance analysis shows that this scheme is feasible to implement in practical voting.
{"title":"A Timed-Release E-Voting Scheme Based on Paillier Homomorphic Encryption","authors":"Ke Yuan;Peng Sang;Jian Ge;Bingcai Zhou;Chunfu Jia","doi":"10.1109/TSUSC.2024.3371544","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3371544","url":null,"abstract":"E-Voting is widely used in many social, economic, political and cultural fields for its convenience, efficiency and greenness, but how to guarantee the fairness of e-voting and the controllability of human intervention needs further in-depth research and exploration. Although the introduction of homomorphic encryption algorithm solves the problem of ballot privacy calculation, and most of these schemes solve the problem of private key confidentiality by using or overlaying multiple different methods of saving private keys, its security will be questioned as long as there is a possibility of human intervention in the saving process. To solve this problem, we propose a timed-release e-voting scheme based on Paillier homomorphic encryption. We analyze the semantic security of the ballot formally by defining the security game, and realize the legitimacy check of the ballot ciphertext through the idea of partial knowledge proof. Property analysis shows that this scheme satisfies the basic properties of the security requirements of the e-voting scheme. Performance analysis shows that this scheme is feasible to implement in practical voting.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"740-753"},"PeriodicalIF":3.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397221","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 : 2024-03-06DOI: 10.1109/TSUSC.2024.3374049
Jinbo Wang;Ruijin Wang;Guangquan Xu;Donglin He;Xikai Pei;Fengli Zhang;Jie Gan
Federated learning is a distributed learning paradigm, which is usually combined with edge computing to meet the joint training of IoT devices. A significant challenge in federated learning lies in the statistical heterogeneity, characterized by non-independent and identically distributed (non-IID) local data across diverse parties. This heterogeneity can result in inconsistent optimization within individual local models. Although previous research has endeavored to tackle issues stemming from heterogeneous data, our findings indicate that these attempts have not yielded high-performance neural network models. To overcome this fundamental challenge, we introduce the framework called FedPKR in this paper, which facilitates efficient federated learning through knowledge review. The core principle of FedPKR involves leveraging the knowledge representation generated by the global and local model layers to conduct periodic layer-by-layer comparative learning in a reciprocal manner. This strategy rectifies local model training, leading to enhanced outcomes. Our experimental results and subsequent analysis substantiate that FedPKR effectively augments model accuracy in image classification tasks, meanwhile demonstrating resilience to statistical heterogeneity across all participating entities.
{"title":"FedPKR: Federated Learning With Non-IID Data via Periodic Knowledge Review in Edge Computing","authors":"Jinbo Wang;Ruijin Wang;Guangquan Xu;Donglin He;Xikai Pei;Fengli Zhang;Jie Gan","doi":"10.1109/TSUSC.2024.3374049","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3374049","url":null,"abstract":"Federated learning is a distributed learning paradigm, which is usually combined with edge computing to meet the joint training of IoT devices. A significant challenge in federated learning lies in the statistical heterogeneity, characterized by non-independent and identically distributed (non-IID) local data across diverse parties. This heterogeneity can result in inconsistent optimization within individual local models. Although previous research has endeavored to tackle issues stemming from heterogeneous data, our findings indicate that these attempts have not yielded high-performance neural network models. To overcome this fundamental challenge, we introduce the framework called FedPKR in this paper, which facilitates efficient federated learning through knowledge review. The core principle of FedPKR involves leveraging the knowledge representation generated by the global and local model layers to conduct periodic layer-by-layer comparative learning in a reciprocal manner. This strategy rectifies local model training, leading to enhanced outcomes. Our experimental results and subsequent analysis substantiate that FedPKR effectively augments model accuracy in image classification tasks, meanwhile demonstrating resilience to statistical heterogeneity across all participating entities.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"902-912"},"PeriodicalIF":3.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810515","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 : 2024-02-28DOI: 10.1109/TSUSC.2024.3370837
Vidushi Agarwal;Omid Ardakanian;Sujata Pal
With the rollout of smart meters, a vast amount of energy time-series became available from homes, enabling applications such as non-intrusive load monitoring (NILM). The inconspicuous collection of this data, however, poses a risk to the privacy of customers. Federated Learning (FL) eliminates the problem of sharing raw data with a cloud service provider by allowing machine learning models to be trained in a collaborative fashion on decentralized data. Although several NILM techniques that rely on FL to train a deep neural network for identifying the energy consumption of individual appliances have been proposed in recent years, the robustness of these techniques to malicious users and their ability to fully protect the user privacy remain unexplored. In this paper, we present a robust and privacy-preserving FL-based framework to train a bidirectional transformer architecture for NILM. This framework takes advantage of a meta-learning algorithm to handle the data heterogeneity prevalent in real-world settings. The efficacy of the proposed framework is corroborated through comparative experiments using two real-world NILM datasets. The results show that this framework can attain an accuracy that is on par with a centrally-trained energy disaggregation model, while preserving user privacy.
{"title":"A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring","authors":"Vidushi Agarwal;Omid Ardakanian;Sujata Pal","doi":"10.1109/TSUSC.2024.3370837","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3370837","url":null,"abstract":"With the rollout of smart meters, a vast amount of energy time-series became available from homes, enabling applications such as non-intrusive load monitoring (NILM). The inconspicuous collection of this data, however, poses a risk to the privacy of customers. Federated Learning (FL) eliminates the problem of sharing raw data with a cloud service provider by allowing machine learning models to be trained in a collaborative fashion on decentralized data. Although several NILM techniques that rely on FL to train a deep neural network for identifying the energy consumption of individual appliances have been proposed in recent years, the robustness of these techniques to malicious users and their ability to fully protect the user privacy remain unexplored. In this paper, we present a robust and privacy-preserving FL-based framework to train a bidirectional transformer architecture for NILM. This framework takes advantage of a meta-learning algorithm to handle the data heterogeneity prevalent in real-world settings. The efficacy of the proposed framework is corroborated through comparative experiments using two real-world NILM datasets. The results show that this framework can attain an accuracy that is on par with a centrally-trained energy disaggregation model, while preserving user privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"766-777"},"PeriodicalIF":3.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397223","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 : 2024-02-23DOI: 10.1109/TSUSC.2024.3369333
Pierre Jacquet;Thomas Ledoux;Romain Rouvoy
Despite continuous improvements, cloud physical resources remain underused, hence severely impacting the efficiency of these infrastructures at large. To overcome this inefficiency, Infrastructure-as-a-Service (IaaS) providers usually compensate for oversized Virtual Machines (VMs) by offering more virtual resources than are physically available on a host. However, this technique—known as oversubscription —may hinder performances when a statically-defined oversubscription ratio results in resource contention of hosted VMs. Therefore, instead of setting a static and cluster-wide ratio, this article studies how a greedy increase of the oversubscription ratio per Physical Machine (PM) and resources type can preserve performance goals. Keeping performance unchanged allows our contribution to be more realistically adopted by production-scale IaaS infrastructures. This contribution, named ScroogeVM , leverages the detection of PM stability to carefully increase the associated oversubscription ratios. Based on metrics shared by public cloud providers, we investigate the impact of resource oversubscription on performance degradation. Subsequently, we conduct a comparative analysis of ScroogeVM with state-of-the-art oversubscription computations. The results demonstrate that our approach outperforms existing methods by leveraging the presence of long-lasting VMs, while avoiding live migration penalties and performance impacts for stakeholders.
{"title":"SCROOGEVM: Boosting Cloud Resource Utilization With Dynamic Oversubscription","authors":"Pierre Jacquet;Thomas Ledoux;Romain Rouvoy","doi":"10.1109/TSUSC.2024.3369333","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3369333","url":null,"abstract":"Despite continuous improvements, cloud physical resources remain underused, hence severely impacting the efficiency of these infrastructures at large. To overcome this inefficiency, Infrastructure-as-a-Service (IaaS) providers usually compensate for oversized Virtual Machines (VMs) by offering more virtual resources than are physically available on a host. However, this technique—known as \u0000<italic>oversubscription</i>\u0000—may hinder performances when a statically-defined oversubscription ratio results in resource contention of hosted VMs. Therefore, instead of setting a static and cluster-wide ratio, this article studies how a greedy increase of the oversubscription ratio per Physical Machine (PM) and resources type can preserve performance goals. Keeping performance unchanged allows our contribution to be more realistically adopted by production-scale IaaS infrastructures. This contribution, named \u0000<sc>ScroogeVM</small>\u0000, leverages the detection of PM stability to carefully increase the associated oversubscription ratios. Based on metrics shared by public cloud providers, we investigate the impact of resource oversubscription on performance degradation. Subsequently, we conduct a comparative analysis of \u0000<sc>ScroogeVM</small>\u0000 with state-of-the-art oversubscription computations. The results demonstrate that our approach outperforms existing methods by leveraging the presence of long-lasting VMs, while avoiding live migration penalties and performance impacts for stakeholders.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"754-765"},"PeriodicalIF":3.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397222","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 : 2024-02-23DOI: 10.1109/TSUSC.2024.3369092
Shuai Guo;Menglei Xia;Huanqun Xue;Shuang Wang;Chao Liu
With the in-depth study of the internal process mechanism of the global ocean by oceanographers, traditional ocean observation methods have been unable to meet the new observation requirements. In order to achieve a low-cost ocean observation mechanism with high spatio-temporal resolution, this paper introduces mobile crowd sensing technology into the field of ocean observation. First, a Transformer-based vessel trajectory prediction algorithm is proposed, which can monitor the location and movement trajectory of vessel in real time. Second, the participant selection algorithm in mobile crowd sensing is studied, and based on the trajectory prediction algorithm, a dynamic participant selection algorithm for ocean mobile crowd sensing is proposed by combining it with the discrete particle swarm optimization (DPSO) algorithm. Third, a coverage estimation algorithm is designed to estimate the coverage of the selection scheme. Finally, the spatio-temporal resolution of the vessel's driving trajectory is analyzed through experiments, which verifies the effectiveness of the algorithm and comprehensively confirms the feasibility of mobile crowd sensing in the field of ocean observation.
{"title":"OceanCrowd: Vessel Trajectory Data-Based Participant Selection for Mobile Crowd Sensing in Ocean Observation","authors":"Shuai Guo;Menglei Xia;Huanqun Xue;Shuang Wang;Chao Liu","doi":"10.1109/TSUSC.2024.3369092","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3369092","url":null,"abstract":"With the in-depth study of the internal process mechanism of the global ocean by oceanographers, traditional ocean observation methods have been unable to meet the new observation requirements. In order to achieve a low-cost ocean observation mechanism with high spatio-temporal resolution, this paper introduces mobile crowd sensing technology into the field of ocean observation. First, a Transformer-based vessel trajectory prediction algorithm is proposed, which can monitor the location and movement trajectory of vessel in real time. Second, the participant selection algorithm in mobile crowd sensing is studied, and based on the trajectory prediction algorithm, a dynamic participant selection algorithm for ocean mobile crowd sensing is proposed by combining it with the discrete particle swarm optimization (DPSO) algorithm. Third, a coverage estimation algorithm is designed to estimate the coverage of the selection scheme. Finally, the spatio-temporal resolution of the vessel's driving trajectory is analyzed through experiments, which verifies the effectiveness of the algorithm and comprehensively confirms the feasibility of mobile crowd sensing in the field of ocean observation.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"889-901"},"PeriodicalIF":3.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810537","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 : 2024-02-16DOI: 10.1109/TSUSC.2024.3366502
Syed Muhammad Danish;Kaiwen Zhang;Fatima Amara;Juan Carlos Oviedo Cepeda;Luis Fernando Rueda Vasquez;Tom Marynowski
Climate change is a major issue that has disastrous impacts on the environment through different causes like the greenhouse gas (GHG) emission. Many energy utilities around the world intend to reduce GHG emissions by promoting different systems including carbon emission trading (CET), renewable energy certificates (RECs), and tradable white certificates (TWCs). However, these systems are centralized, highly regulated, and operationally expensive and do not meet transparency, trust and security requirements. Accordingly, GHG emission reduction schemes are gradually moving towards blockchain-based solutions due to their underpinning characteristics including decentralization, transparency, anonymity, and trust (independent from third parties). This paper performs a comprehensive investigation into the blockchain technology, deployed for GHG emission reduction plans. It explores existing blockchain solutions along with their associated challenges to effectively uncover their potentials. As a result, this study suggests possible lines of research for future enhancements of blockchain systems particularly their incorporation in GHG emission reduction.
{"title":"Blockchain for Energy Credits and Certificates: A Comprehensive Review","authors":"Syed Muhammad Danish;Kaiwen Zhang;Fatima Amara;Juan Carlos Oviedo Cepeda;Luis Fernando Rueda Vasquez;Tom Marynowski","doi":"10.1109/TSUSC.2024.3366502","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3366502","url":null,"abstract":"Climate change is a major issue that has disastrous impacts on the environment through different causes like the greenhouse gas (GHG) emission. Many energy utilities around the world intend to reduce GHG emissions by promoting different systems including carbon emission trading (CET), renewable energy certificates (RECs), and tradable white certificates (TWCs). However, these systems are centralized, highly regulated, and operationally expensive and do not meet transparency, trust and security requirements. Accordingly, GHG emission reduction schemes are gradually moving towards blockchain-based solutions due to their underpinning characteristics including decentralization, transparency, anonymity, and trust (independent from third parties). This paper performs a comprehensive investigation into the blockchain technology, deployed for GHG emission reduction plans. It explores existing blockchain solutions along with their associated challenges to effectively uncover their potentials. As a result, this study suggests possible lines of research for future enhancements of blockchain systems particularly their incorporation in GHG emission reduction.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"727-739"},"PeriodicalIF":3.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397220","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 : 2024-02-06DOI: 10.1109/TSUSC.2024.3362697
Yiming Wang;Meng Hao;Hui He;Weizhe Zhang;Qiuyuan Tang;Xiaoyang Sun;Zheng Wang
Power and energy consumption is the limiting factor of modern computing systems. As the GPU becomes a mainstream computing device, power management for GPUs becomes increasingly important. Current works focus on GPU kernel-level power management, with challenges in portability due to architecture-specific considerations. We present DRLCap , a general runtime power management framework intended to support power management across various GPU architectures. It periodically monitors system-level information to dynamically detect program phase changes and model the workload and GPU system behavior. This elimination from kernel-specific constraints enhances adaptability and responsiveness. The framework leverages dynamic GPU frequency capping, which is the most widely used power knob, to control the power consumption. DRLCap employs deep reinforcement learning (DRL) to adapt to the changing of program phases by automatically adjusting its power policy through online learning, aiming to reduce the GPU power consumption without significantly compromising the application performance. We evaluate DRLCap on three NVIDIA and one AMD GPU architectures. Experimental results show that DRLCap improves prior GPU power optimization strategies by a large margin. On average, it reduces the GPU energy consumption by 22% with less than 3% performance slowdown on NVIDIA GPUs. This translates to a 20% improvement in the energy efficiency measured by the energy-delay product (EDP) over the NVIDIA default GPU power management strategy. For the AMD GPU architecture, DRLCap saves energy consumption by 10%, on average, with a 4% percentage loss, and improves energy efficiency by 8%.
{"title":"DRLCAP: Runtime GPU Frequency Capping With Deep Reinforcement Learning","authors":"Yiming Wang;Meng Hao;Hui He;Weizhe Zhang;Qiuyuan Tang;Xiaoyang Sun;Zheng Wang","doi":"10.1109/TSUSC.2024.3362697","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3362697","url":null,"abstract":"Power and energy consumption is the limiting factor of modern computing systems. As the GPU becomes a mainstream computing device, power management for GPUs becomes increasingly important. Current works focus on GPU kernel-level power management, with challenges in portability due to architecture-specific considerations. We present \u0000<sc>DRLCap</small>\u0000, a general runtime power management framework intended to support power management across various GPU architectures. It periodically monitors system-level information to dynamically detect program phase changes and model the workload and GPU system behavior. This elimination from kernel-specific constraints enhances adaptability and responsiveness. The framework leverages dynamic GPU frequency capping, which is the most widely used power knob, to control the power consumption. \u0000<sc>DRLCap</small>\u0000 employs deep reinforcement learning (DRL) to adapt to the changing of program phases by automatically adjusting its power policy through online learning, aiming to reduce the GPU power consumption without significantly compromising the application performance. We evaluate \u0000<sc>DRLCap</small>\u0000 on three NVIDIA and one AMD GPU architectures. Experimental results show that \u0000<sc>DRLCap</small>\u0000 improves prior GPU power optimization strategies by a large margin. On average, it reduces the GPU energy consumption by 22% with less than 3% performance slowdown on NVIDIA GPUs. This translates to a 20% improvement in the energy efficiency measured by the energy-delay product (EDP) over the NVIDIA default GPU power management strategy. For the AMD GPU architecture, \u0000<sc>DRLCap</small>\u0000 saves energy consumption by 10%, on average, with a 4% percentage loss, and improves energy efficiency by 8%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"712-726"},"PeriodicalIF":3.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397260","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 : 2024-02-05DOI: 10.1109/TSUSC.2024.3362074
Ke Gu;XingQiang Wang;Xiong Li
The security of fog computing has been researched and concerned with its development, where malicious attacks pose a greater threat to distributed data storage based on fog computing. Also, the rapid increasing on the number of terminal devices has raised the importance of fog computing-based distributed data storage. In response to this demand, it is essential to establish a secure and privacy-preserving distributed data auditing method that enables security protection of stored data and effective control over identities of auditors. In this paper, we propose a dynamic outsourced data audit scheme for Merkle hash grid-based fog storage with privacy-preserving, where fog servers are used to undertake partial outsourced computation and data storage. Our scheme can provide the function of privacy-preserving for outsourced data by blinding original stored data, and supports data owners to define their auditing access policies by the linear secret-sharing scheme to control the identities of auditors. Further, the construction of Merkle hash grid is used to improve the efficiency of dynamic data operations. Also, a server locating approach is proposed to enable the third-part auditor to identify specific malicious data fog servers within distributed data storage. Under the proposed security model, the security of our scheme can be proved, which can further provide collusion resistance and privacy-preserving for outsourced data. Additionally, both theoretical and experimental evaluations illustrate the efficiency of our proposed scheme.
{"title":"Dynamic Outsourced Data Audit Scheme for Merkle Hash Grid-Based Fog Storage With Privacy-Preserving","authors":"Ke Gu;XingQiang Wang;Xiong Li","doi":"10.1109/TSUSC.2024.3362074","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3362074","url":null,"abstract":"The security of fog computing has been researched and concerned with its development, where malicious attacks pose a greater threat to distributed data storage based on fog computing. Also, the rapid increasing on the number of terminal devices has raised the importance of fog computing-based distributed data storage. In response to this demand, it is essential to establish a secure and privacy-preserving distributed data auditing method that enables security protection of stored data and effective control over identities of auditors. In this paper, we propose a dynamic outsourced data audit scheme for Merkle hash grid-based fog storage with privacy-preserving, where fog servers are used to undertake partial outsourced computation and data storage. Our scheme can provide the function of privacy-preserving for outsourced data by blinding original stored data, and supports data owners to define their auditing access policies by the linear secret-sharing scheme to control the identities of auditors. Further, the construction of Merkle hash grid is used to improve the efficiency of dynamic data operations. Also, a server locating approach is proposed to enable the third-part auditor to identify specific malicious data fog servers within distributed data storage. Under the proposed security model, the security of our scheme can be proved, which can further provide collusion resistance and privacy-preserving for outsourced data. Additionally, both theoretical and experimental evaluations illustrate the efficiency of our proposed scheme.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"695-711"},"PeriodicalIF":3.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965829","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 : 2024-02-01DOI: 10.1109/TSUSC.2024.3360975
Fu Jiang;Yaoxin Xia;Lisen Yan;Weirong Liu;Xiaoyong Zhang;Heng Li;Jun Peng
Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. First, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.
{"title":"Battery-Aware Workflow Scheduling for Portable Heterogeneous Computing","authors":"Fu Jiang;Yaoxin Xia;Lisen Yan;Weirong Liu;Xiaoyong Zhang;Heng Li;Jun Peng","doi":"10.1109/TSUSC.2024.3360975","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3360975","url":null,"abstract":"Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. First, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"677-694"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965765","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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