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}
Pub Date : 2024-12-11DOI: 10.1109/TSUSC.2024.3506822
Jiepin Ding;Jun Xia;Yaning Yang;Junlong Zhou;Mingsong Chen;Keqin Li
Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521 s to 475 s.
{"title":"Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization","authors":"Jiepin Ding;Jun Xia;Yaning Yang;Junlong Zhou;Mingsong Chen;Keqin Li","doi":"10.1109/TSUSC.2024.3506822","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3506822","url":null,"abstract":"Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521 s to 475 s.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"601-615"},"PeriodicalIF":3.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219630","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 : 2024-11-25DOI: 10.1109/TSUSC.2024.3503712
Ziheng Xiao;Chenlu Zhu;Wei Feng;Shenghao Liu;Xianjun Deng;Hongwei Lu;Laurence T. Yang;Jong Hyuk Park
Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.
物联网可靠性评估有助于实现网络的持续计算，增强网络的稳定性。以前的算法通常通过枚举节点和网络的状态来评估物联网的可靠性，由于计算成本的原因，难以处理数百个节点的物联网。本文提出了一种新的TMCRA算法来评估复杂网络环境下物联网的可靠性，同时考虑了覆盖和连通性。在覆盖方面，TMCRA采用CIC （confidence Information coverage）模型将目标区域划分为独立的网格并计算覆盖率。在连通性方面，TMCRA基于TMA和MGIN两种提出的方法组成虚拟骨干网（VBN），并通过分析VBN而不是整个网络来评估连通性。TMA和MGIN是构造最小连通优势集（MCDS）的两种算法，适用于不同规模的网络。最后，TMCRA基于覆盖和连通性数据，利用张量对网络结构进行统一建模和表示，并基于张量计算物联网可靠性。对不同规模的物联网进行了仿真，以证明该方法在可靠性评估中的优势和有效性。
{"title":"Tensor and Minimum Connected Dominating Set Based Confident Information Coverage Reliability Evaluation for IoT","authors":"Ziheng Xiao;Chenlu Zhu;Wei Feng;Shenghao Liu;Xianjun Deng;Hongwei Lu;Laurence T. Yang;Jong Hyuk Park","doi":"10.1109/TSUSC.2024.3503712","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3503712","url":null,"abstract":"Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"547-561"},"PeriodicalIF":3.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219804","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 : 2024-11-25DOI: 10.1109/TSUSC.2024.3506213
Jing Bai;Xiaolin Chang;Fumio Machida;Kishor S. Trivedi
Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markov-based approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.
{"title":"Understanding Container-Based Services Under Software Aging: Dependability and Performance Views","authors":"Jing Bai;Xiaolin Chang;Fumio Machida;Kishor S. Trivedi","doi":"10.1109/TSUSC.2024.3506213","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3506213","url":null,"abstract":"Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markov-based approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"562-575"},"PeriodicalIF":3.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219806","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 : 2024-11-21DOI: 10.1109/TSUSC.2024.3503580
Jie Zhang;Zhihao Zhang;Degan Zhang;Chenhao Ni;Ting Zhang;Xingru Jiang
As an important branch of the Internet of Things (IoT), vehicular networks play a crucial role in the construction of intelligent transportation systems. However, due to the rapid movement of vehicles and signal obstruction, achieving high- quality and low-latency communication in vehicular networks remains a significant challenge. To address this issue, this paper proposes a novel data communication method based on interval type-2 fuzzy logic and cooperative game theory. Firstly, interval type-2 fuzzy logic is utilized to infer vehicle stability, thereby selecting high-quality backbone nodes. Concurrently, the memory and forgetfulness functions of the Gated Recurrent Unit are employed to retain critical data packets. Subsequently, a greedy algorithm and cooperative game theory model are used to describe the behavior of vehicles in Roadside-to-Vehicle (R2V) communication and Vehicle-to- Vehicle (V2V) communication, respectively. This approach encourages backbone nodes to cooperate and serve other vehicles based on a benefit function. Experimental results demonstrate that the proposed method excels in terms of transmission delay, coverage range, and data packet delivery success rate.
{"title":"Novel Approach of Vehicular Cooperative Communication Based on Strategy of Interval Type-2 Fuzzy Logic and Cooperative Game","authors":"Jie Zhang;Zhihao Zhang;Degan Zhang;Chenhao Ni;Ting Zhang;Xingru Jiang","doi":"10.1109/TSUSC.2024.3503580","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3503580","url":null,"abstract":"As an important branch of the Internet of Things (IoT), vehicular networks play a crucial role in the construction of intelligent transportation systems. However, due to the rapid movement of vehicles and signal obstruction, achieving high- quality and low-latency communication in vehicular networks remains a significant challenge. To address this issue, this paper proposes a novel data communication method based on interval type-2 fuzzy logic and cooperative game theory. Firstly, interval type-2 fuzzy logic is utilized to infer vehicle stability, thereby selecting high-quality backbone nodes. Concurrently, the memory and forgetfulness functions of the Gated Recurrent Unit are employed to retain critical data packets. Subsequently, a greedy algorithm and cooperative game theory model are used to describe the behavior of vehicles in Roadside-to-Vehicle (R2V) communication and Vehicle-to- Vehicle (V2V) communication, respectively. This approach encourages backbone nodes to cooperate and serve other vehicles based on a benefit function. Experimental results demonstrate that the proposed method excels in terms of transmission delay, coverage range, and data packet delivery success rate.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"588-600"},"PeriodicalIF":3.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219581","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 : 2024-11-21DOI: 10.1109/TSUSC.2024.3504242
Nikolaos Pavlidis;Vasileios Perifanis;Selim F. Yilmaz;Francesc Wilhelmi;Marco Miozzo;Pavlos S. Efraimidis;Remous-Aris Koutsiamanis;Pavol Mulinka;Paolo Dini
The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.
{"title":"Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting","authors":"Nikolaos Pavlidis;Vasileios Perifanis;Selim F. Yilmaz;Francesc Wilhelmi;Marco Miozzo;Pavlos S. Efraimidis;Remous-Aris Koutsiamanis;Pavol Mulinka;Paolo Dini","doi":"10.1109/TSUSC.2024.3504242","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3504242","url":null,"abstract":"The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"576-587"},"PeriodicalIF":3.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219807","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 : 2024-11-13DOI: 10.1109/TSUSC.2024.3497326
Xinmin Zhang;Song Wu;Lin Gu;Qiang He;Hai Jin
This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-the-art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%.
{"title":"ChestBox: Enabling Fast State Sharing for Stateful Serverless Computing With State Functions","authors":"Xinmin Zhang;Song Wu;Lin Gu;Qiang He;Hai Jin","doi":"10.1109/TSUSC.2024.3497326","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3497326","url":null,"abstract":"This paper presents ChestBox, a novel approach that utilizes <italic>state functions</i> to facilitate low-latency state sharing for stateful serverless computing. When an <italic>application function</i> needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-the-art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"491-502"},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219803","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}
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