Pub Date : 2025-01-28DOI: 10.1016/j.future.2025.107726
Yinfu Deng , Hua Dai , Zhangchen Li , Haiping Huang , Qian Zhou , Jian Xu , Geng Yang
Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing will be accelerated. To improve the search efficiency, we propose an encrypted two-layer balance binary tree index-based multi-keyword ranked search scheme (ETMRS) to address this problem in this paper. An equal-length dictionary partition-based compressed vector space model (EDP-CVSM) is presented, which introduces the dictionary partition strategy. It effectively compresses the document and search vectors, which benefits the efficiency of relevance score computation in search processing. In addition, to further improves the search efficiency, a two-layer balance binary tree index (TBBT-index) is proposed, which adopts secure inner product and symmetric encryption to preserve the privacy. The index is able to filter out the sub-dictionaries having no search keywords in the upper layer and identify the result documents in the lower layer, which speeds up the search processing. Experimental results show a good performance of the proposed scheme in file coverage rate, search precision, rank privacy, search efficiency and space consumption.
{"title":"EDP-CVSM model-based multi-keyword ranked search scheme over encrypted cloud data","authors":"Yinfu Deng , Hua Dai , Zhangchen Li , Haiping Huang , Qian Zhou , Jian Xu , Geng Yang","doi":"10.1016/j.future.2025.107726","DOIUrl":"10.1016/j.future.2025.107726","url":null,"abstract":"<div><div>Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing will be accelerated. To improve the search efficiency, we propose an encrypted two-layer balance binary tree index-based multi-keyword ranked search scheme (ETMRS) to address this problem in this paper. An equal-length dictionary partition-based compressed vector space model (EDP-CVSM) is presented, which introduces the dictionary partition strategy. It effectively compresses the document and search vectors, which benefits the efficiency of relevance score computation in search processing. In addition, to further improves the search efficiency, a two-layer balance binary tree index (TBBT-index) is proposed, which adopts secure inner product and symmetric encryption to preserve the privacy. The index is able to filter out the sub-dictionaries having no search keywords in the upper layer and identify the result documents in the lower layer, which speeds up the search processing. Experimental results show a good performance of the proposed scheme in file coverage rate, search precision, rank privacy, search efficiency and space consumption.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107726"},"PeriodicalIF":6.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-27DOI: 10.1016/j.future.2025.107733
Min Wang , Jiawang Chen , Haoyuan Wang , Ziyi Gao , Weihao Bian , Sibo Qiao
Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attention to load balancing during processor allocation. To solve these problems, this study introduces the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, a novel list scheduling approach to improve the scheduling efficiency of compute-heavy tasks in heterogeneous systems. The BPPTS algorithm proposes the Balanced Prediction Cost Matrix (BPCM), which comprehensively evaluates the importance of tasks by considering their average computation cost. At the same time, a computation enhancement factor is introduced in the priority sorting to optimize the scheduling of computation-intensive tasks. The goal is to improve the scheduling efficiency of computation-intensive tasks and achieve load balancing. The BPPTS algorithm has a complexity of , where represents the number of tasks, and denotes the number of processors. Experiments demonstrate that BPPTS outperforms other algorithms in terms of maximum completion time and speedup.
{"title":"An enhanced list scheduling algorithm for heterogeneous computing using an optimized Predictive Cost Matrix","authors":"Min Wang , Jiawang Chen , Haoyuan Wang , Ziyi Gao , Weihao Bian , Sibo Qiao","doi":"10.1016/j.future.2025.107733","DOIUrl":"10.1016/j.future.2025.107733","url":null,"abstract":"<div><div>Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attention to load balancing during processor allocation. To solve these problems, this study introduces the Balanced Prediction Priority Task Scheduling (BPPTS) algorithm, a novel list scheduling approach to improve the scheduling efficiency of compute-heavy tasks in heterogeneous systems. The BPPTS algorithm proposes the Balanced Prediction Cost Matrix (BPCM), which comprehensively evaluates the importance of tasks by considering their average computation cost. At the same time, a computation enhancement factor is introduced in the priority sorting to optimize the scheduling of computation-intensive tasks. The goal is to improve the scheduling efficiency of computation-intensive tasks and achieve load balancing. The BPPTS algorithm has a complexity of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>v</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>p</mi><mo>)</mo></mrow></mrow></math></span>, where <span><math><mi>v</mi></math></span> represents the number of tasks, and <span><math><mi>p</mi></math></span> denotes the number of processors. Experiments demonstrate that BPPTS outperforms other algorithms in terms of maximum completion time and speedup.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107733"},"PeriodicalIF":6.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and real-time traffic flow prediction is an indispensable part of the intelligent transportation system and is essential in improving traffic planning capability. However, due to the highly nonlinear and spatiotemporal fluctuation characteristics of the large-scale traffic network data, it is a challenging issue to establish an accurate and effective prediction model. In this regard, a hybrid fuzzy grammar dynamic graph diffusing attention network is proposed for traffic flow prediction. Firstly, the network utilizes the grammar network structure composed of grammar rules to synchronously capture the interactive information of observable traffic parameters and the dynamic spatio-temporal correlation of each node. Secondly, the network utilizes an improved graph attention network for spatio-temporal node aggregation and dynamic edge information extraction, effectively mitigating over-smoothing. Finally, the network combines hidden features captured by the grammar structure with the change rate of the traffic flow through the fuzzy network to deduce the blend of hidden features of observable and unobservable information. Simulation results on three real datasets show that the proposed model outperforms existing prediction methods under traffic networks.
{"title":"Hybrid fuzzy grammar dynamic graph diffusing attention network for traffic flow prediction","authors":"Dongxue Zhang , Zhao Zhang , Xiaohong Jiao , Yahui Zhang","doi":"10.1016/j.future.2025.107725","DOIUrl":"10.1016/j.future.2025.107725","url":null,"abstract":"<div><div>Accurate and real-time traffic flow prediction is an indispensable part of the intelligent transportation system and is essential in improving traffic planning capability. However, due to the highly nonlinear and spatiotemporal fluctuation characteristics of the large-scale traffic network data, it is a challenging issue to establish an accurate and effective prediction model. In this regard, a hybrid fuzzy grammar dynamic graph diffusing attention network is proposed for traffic flow prediction. Firstly, the network utilizes the grammar network structure composed of grammar rules to synchronously capture the interactive information of observable traffic parameters and the dynamic spatio-temporal correlation of each node. Secondly, the network utilizes an improved graph attention network for spatio-temporal node aggregation and dynamic edge information extraction, effectively mitigating over-smoothing. Finally, the network combines hidden features captured by the grammar structure with the change rate of the traffic flow through the fuzzy network to deduce the blend of hidden features of observable and unobservable information. Simulation results on three real datasets show that the proposed model outperforms existing prediction methods under traffic networks.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107725"},"PeriodicalIF":6.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-27DOI: 10.1016/j.future.2025.107734
Junchi Ma, Yuzhu Ding, Sulei Huang, Zongtao Duan, Lei Tang
The continuous advancement of technology has led to increasingly complex computing systems, but it has also made them more susceptible to soft errors. Among the challenges posed by soft errors, silent data corruption (SDC) stands out as a particularly insidious threat, often occurring without warning. Estimating SDC probabilities for a program is a formidable task due to the diversity of inputs it can encounter, resulting in significant variations in these probabilities. This paper introduces TEMPORISE, a novel approach designed to tackle this challenge. TEMPORISE leverages the control data flow graph and calling context tree to represent the commonalities and distinctions between different input executions. The embeddings of these graphs are learned through structured graph attention network and AttrE2vec. These embeddings are then combined and input into a regression model to calculate SDC probabilities. The experiments demonstrate that TEMPORISE excels in predicting SDC probabilities, achieving a 78.4 % reduction in mean absolute error compared to vTRIDENT, the state-of-the-art baseline model. Moreover, TEMPORISE improves the rank correlation of SDC probabilities for various inputs by 11.4 % compared to vTRIDENT, indicating its superior ability to capture the relative ordering of SDC probabilities. In terms of computational efficiency, TEMPORISE boasts an impressive 91.3 % reduction in time cost compared to the traditional fault injection approach.
{"title":"TEMPORISE: Extracting semantic representations of varied input executions for silent data corruption evaluation","authors":"Junchi Ma, Yuzhu Ding, Sulei Huang, Zongtao Duan, Lei Tang","doi":"10.1016/j.future.2025.107734","DOIUrl":"10.1016/j.future.2025.107734","url":null,"abstract":"<div><div>The continuous advancement of technology has led to increasingly complex computing systems, but it has also made them more susceptible to soft errors. Among the challenges posed by soft errors, silent data corruption (SDC) stands out as a particularly insidious threat, often occurring without warning. Estimating SDC probabilities for a program is a formidable task due to the diversity of inputs it can encounter, resulting in significant variations in these probabilities. This paper introduces TEMPORISE, a novel approach designed to tackle this challenge. TEMPORISE leverages the control data flow graph and calling context tree to represent the commonalities and distinctions between different input executions. The embeddings of these graphs are learned through structured graph attention network and AttrE2vec. These embeddings are then combined and input into a regression model to calculate SDC probabilities. The experiments demonstrate that TEMPORISE excels in predicting SDC probabilities, achieving a 78.4 % reduction in mean absolute error compared to vTRIDENT, the state-of-the-art baseline model. Moreover, TEMPORISE improves the rank correlation of SDC probabilities for various inputs by 11.4 % compared to vTRIDENT, indicating its superior ability to capture the relative ordering of SDC probabilities. In terms of computational efficiency, TEMPORISE boasts an impressive 91.3 % reduction in time cost compared to the traditional fault injection approach.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107734"},"PeriodicalIF":6.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-27DOI: 10.1016/j.future.2025.107727
Luis M. Moreno-Saavedra , Vinícius G. Costa , Adrián Garrido-Sáez , Silvia Jiménez-Fernández , J. Antonio Portilla-Figueras , Sancho Salcedo-Sanz
The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors’ placement and convergence time of the algorithm.
{"title":"Evolutionary optimization of spatially-distributed multi-sensors placement for indoor surveillance environments with security levels","authors":"Luis M. Moreno-Saavedra , Vinícius G. Costa , Adrián Garrido-Sáez , Silvia Jiménez-Fernández , J. Antonio Portilla-Figueras , Sancho Salcedo-Sanz","doi":"10.1016/j.future.2025.107727","DOIUrl":"10.1016/j.future.2025.107727","url":null,"abstract":"<div><div>The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors’ placement and convergence time of the algorithm.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107727"},"PeriodicalIF":6.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-27DOI: 10.1016/j.future.2025.107728
Zhenshan Bao, Tianhao Zang, Yiqi Liu, Wenbo Zhang
Lattice-based cryptography, essential for fully homomorphic encryption, primarily relies on the computationally intensive Number Theoretic Transform (NTT). This paper proposes an NTT accelerator based on AMD/Xilinx Versal ACAP and AI Engine (AIE), featuring data engines on Programmable Logic (PL) and compute engines on the AIE. For inter-core parallelism on the AIE array, we propose an efficient method that applies the communication avoidance strategy to meet resource constraints; for intra-core data parallelism, we explore the modular multiplication algorithm suitable for AIE’s SIMD processors, proposing optimized software to support extensive NTT parameters while ensuring efficiency. Specialized data units are also proposed to compensate the slow DDR interface, enhancing data flow and overall performance. Our design outperforms CPU-based solutions by an average of 8.30 and Tesla V100 GPU-based solutions by 1.44 to 1.89. Compared to most FPGA-based solutions, our approach shows shorter latency, improving by an average of 2.62, while ensuring scalability and flexibility.
{"title":"Efficient Number Theoretic Transform accelerator on the versal platform powered by the AI Engine","authors":"Zhenshan Bao, Tianhao Zang, Yiqi Liu, Wenbo Zhang","doi":"10.1016/j.future.2025.107728","DOIUrl":"10.1016/j.future.2025.107728","url":null,"abstract":"<div><div>Lattice-based cryptography, essential for fully homomorphic encryption, primarily relies on the computationally intensive Number Theoretic Transform (NTT). This paper proposes an NTT accelerator based on AMD/Xilinx Versal ACAP and AI Engine (AIE), featuring data engines on Programmable Logic (PL) and compute engines on the AIE. For inter-core parallelism on the AIE array, we propose an efficient method that applies the communication avoidance strategy to meet resource constraints; for intra-core data parallelism, we explore the modular multiplication algorithm suitable for AIE’s SIMD processors, proposing optimized software to support extensive NTT parameters while ensuring efficiency. Specialized data units are also proposed to compensate the slow DDR interface, enhancing data flow and overall performance. Our design outperforms CPU-based solutions by an average of 8.30<span><math><mo>×</mo></math></span> and Tesla V100 GPU-based solutions by 1.44<span><math><mo>×</mo></math></span> to 1.89<span><math><mo>×</mo></math></span>. Compared to most FPGA-based solutions, our approach shows shorter latency, improving by an average of 2.62<span><math><mo>×</mo></math></span>, while ensuring scalability and flexibility.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107728"},"PeriodicalIF":6.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-26DOI: 10.1016/j.future.2025.107730
Mariano Garralda-Barrio, Carlos Eiras-Franco, Verónica Bolón-Canedo
The rise of data-intensive scalable computing systems, such as Apache Spark, has transformed data processing by enabling the efficient manipulation of large datasets across machine clusters. However, system configuration to optimize performance remains a challenge. This paper introduces an adaptive incremental transfer learning approach to predicting workload execution times. By integrating both unsupervised and supervised learning, we develop models that adapt incrementally to new workloads and configurations. To guide the optimal selection of relevant workloads, the model employs the coefficient of distance variation (CdV) and the coefficient of quality correlation (CqC), combined in the exploration–exploitation balance coefficient (EEBC). Comprehensive evaluations demonstrate the robustness and reliability of our model for performance modeling in Spark applications, with average improvements of up to 31% over state-of-the-art methods. This research contributes to efficient performance tuning systems by enabling transfer learning from historical workloads to new, previously unseen workloads. The full source code is openly available.
{"title":"Adaptive incremental transfer learning for efficient performance modeling of big data workloads","authors":"Mariano Garralda-Barrio, Carlos Eiras-Franco, Verónica Bolón-Canedo","doi":"10.1016/j.future.2025.107730","DOIUrl":"10.1016/j.future.2025.107730","url":null,"abstract":"<div><div>The rise of data-intensive scalable computing systems, such as Apache Spark, has transformed data processing by enabling the efficient manipulation of large datasets across machine clusters. However, system configuration to optimize performance remains a challenge. This paper introduces an adaptive incremental transfer learning approach to predicting workload execution times. By integrating both unsupervised and supervised learning, we develop models that adapt incrementally to new workloads and configurations. To guide the optimal selection of relevant workloads, the model employs the coefficient of distance variation (CdV) and the coefficient of quality correlation (CqC), combined in the exploration–exploitation balance coefficient (EEBC). Comprehensive evaluations demonstrate the robustness and reliability of our model for performance modeling in Spark applications, with average improvements of up to 31% over state-of-the-art methods. This research contributes to efficient performance tuning systems by enabling transfer learning from historical workloads to new, previously unseen workloads. The full source code is openly available.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107730"},"PeriodicalIF":6.2,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-25DOI: 10.1016/j.future.2025.107731
Lin Qiu , Bo Yi , Xingwei Wang , Fei Gao , Kaimin Zhang , Yanpeng Qu , Min Huang
The problem of insufficient storage space caused by the full-replication mechanism, which is commonly employed in existing blockchains, poses an obstacle to system scalability. Moreover, existing storage sharding mechanisms are confronted with the risk of data tampering by reason of the existence of Byzantine nodes. To address the above problems, the storage partition mechanisms, integrating Erasure Coding with Byzantine Fault Tolerance consensus protocol, are proposed such as BFT-Store and PartitionChain. While promising, these solutions still encounter three significant challenges. First, the substantial computational complexity associated with encoding during data storage and decoding during data recovery will impede the efficiency (e.g., latency and throughput) of the permissioned blockchain. Second, the signature schemes employed for verifying the completeness and correctness of encoded data on each node lead to massive communication over the network, thereby further limiting the system efficiency. Third, the process of system re-initialization, which necessitates the participation of all nodes, degrades the system stability. This paper proposes a Multi-group Collaborative Parallel Data Storage Mechanism for Permissioned Blockchain Sharding called GPartition-Store to alleviate the above problems, where the nodes are divided into multiple Storage Groups (SGs). First, the original block is partitioned into sub-blocks (assuming is the number of SGs), with each sub-block being further partitioned and encoded into smaller encoded-blocks or recovered by decoding in parallel across all SGs. Hence, the computational complexity of coding (i.e., encoding and decoding) can be decreased by about and times respectively. Second, the bloom filter is utilized to generate the verification proofs of the sub-blocks and encoded-block sets, which simultaneously avoids the heavy amount of transmitted messages, while liberating the requirement for dependence on any trusted third party. Third, the re-initialization process is launched exclusively within a specific SG when a node joins/quits the system or a single crashed node needs repair, thereby enhancing the system stability. Compared with the full-replication mechanism, BFT-Store and PartitionChain, the experimental results illustrate that GPartition-Store can improve the scalability, efficiency and stability of the dynamic blockchain network while maintaining the availability of the blocks.
{"title":"GPartition-store: A multi-group collaborative parallel data storage mechanism for permissioned blockchain sharding","authors":"Lin Qiu , Bo Yi , Xingwei Wang , Fei Gao , Kaimin Zhang , Yanpeng Qu , Min Huang","doi":"10.1016/j.future.2025.107731","DOIUrl":"10.1016/j.future.2025.107731","url":null,"abstract":"<div><div>The problem of insufficient storage space caused by the full-replication mechanism, which is commonly employed in existing blockchains, poses an obstacle to system scalability. Moreover, existing storage sharding mechanisms are confronted with the risk of data tampering by reason of the existence of Byzantine nodes. To address the above problems, the storage partition mechanisms, integrating Erasure Coding with Byzantine Fault Tolerance consensus protocol, are proposed such as BFT-Store and PartitionChain. While promising, these solutions still encounter three significant challenges. First, the substantial computational complexity associated with encoding during data storage and decoding during data recovery will impede the efficiency (e.g., latency and throughput) of the permissioned blockchain. Second, the signature schemes employed for verifying the completeness and correctness of encoded data on each node lead to massive communication over the network, thereby further limiting the system efficiency. Third, the process of system re-initialization, which necessitates the participation of all nodes, degrades the system stability. This paper proposes a Multi-group Collaborative Parallel Data Storage Mechanism for Permissioned Blockchain Sharding called GPartition-Store to alleviate the above problems, where the nodes are divided into multiple Storage Groups (SGs). First, the original block is partitioned into <span><math><mi>g</mi></math></span> sub-blocks (assuming <span><math><mi>g</mi></math></span> is the number of SGs), with each sub-block being further partitioned and encoded into smaller encoded-blocks or recovered by decoding in parallel across all SGs. Hence, the computational complexity of coding (i.e., encoding and decoding) can be decreased by about <span><math><msup><mrow><mi>g</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and <span><math><msup><mrow><mi>g</mi></mrow><mrow><mn>3</mn></mrow></msup></math></span> times respectively. Second, the bloom filter is utilized to generate the verification proofs of the sub-blocks and encoded-block sets, which simultaneously avoids the heavy amount of transmitted messages, while liberating the requirement for dependence on any trusted third party. Third, the re-initialization process is launched exclusively within a specific SG when a node joins/quits the system or a single crashed node needs repair, thereby enhancing the system stability. Compared with the full-replication mechanism, BFT-Store and PartitionChain, the experimental results illustrate that GPartition-Store can improve the scalability, efficiency and stability of the dynamic blockchain network while maintaining the availability of the blocks.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107731"},"PeriodicalIF":6.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-25DOI: 10.1016/j.future.2025.107724
Ziqian Lin , Xuefeng Jiang , Kun Zhang , Chongjun Fan , Yaya Liu
Federated learning (FL) has recently achieved successes in privacy-sensitive health-care applications like medical analysis. Most previous studies suppose that collected user data are well-annotated, however, it is a strong assumption in practice. For instance, human activity recognition (HAR) task aims to train a model which predicts a certain person’s activity based on sensor data series collected from a given period of time. Due to diverse and incomplete annotation approaches, user-side data inevitably contain significant label noise, which greatly degrade model convergence and performance. In this work, we propose a novel FL framework FedDSHAR, which partitions the user-side data into the clean data subset and noisy data subset. Two strategies are utilized on two subsets to further exploit extra effective information from data, where strategic time-series augmentation is adopted on the clean subset and the semi-supervised learning scheme is used for the noisy subset. Extensive experiments conducted on three public real-world HAR datasets demonstrate that FedDSHAR outperforms six state-of-the-art methods, particularly in addressing extreme label noise in real-world distributed noisy HAR scenarios. Our code is available at https://github.com/coke2020ice/FedDSHAR.
{"title":"FedDSHAR: A dual-strategy federated learning approach for human activity recognition amid noise label user","authors":"Ziqian Lin , Xuefeng Jiang , Kun Zhang , Chongjun Fan , Yaya Liu","doi":"10.1016/j.future.2025.107724","DOIUrl":"10.1016/j.future.2025.107724","url":null,"abstract":"<div><div>Federated learning (FL) has recently achieved successes in privacy-sensitive health-care applications like medical analysis. Most previous studies suppose that collected user data are well-annotated, however, it is a strong assumption in practice. For instance, human activity recognition (HAR) task aims to train a model which predicts a certain person’s activity based on sensor data series collected from a given period of time. Due to diverse and incomplete annotation approaches, user-side data inevitably contain significant label noise, which greatly degrade model convergence and performance. In this work, we propose a novel FL framework FedDSHAR, which partitions the user-side data into the clean data subset and noisy data subset. Two strategies are utilized on two subsets to further exploit extra effective information from data, where strategic time-series augmentation is adopted on the clean subset and the semi-supervised learning scheme is used for the noisy subset. Extensive experiments conducted on three public real-world HAR datasets demonstrate that FedDSHAR outperforms six state-of-the-art methods, particularly in addressing extreme label noise in real-world distributed noisy HAR scenarios. Our code is available at <span><span>https://github.com/coke2020ice/FedDSHAR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107724"},"PeriodicalIF":6.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-25DOI: 10.1016/j.future.2025.107732
Qiwei Hu , Miguel Correia , Tao Jiang
Blockchain-enabled Policy Decision Point (PDP) has been a promising solution to the centralization concern in practical deployment of Attribute-Based Access Control (ABAC). However, existing blockchain systems cannot support PDP adequately since PDP functionalities introduce extra latency to blockchain’s execution process and limits system throughput. This paper proposes an efficient PDP Blockchain (PDPB) by exploiting a minimum-redundancy execution paradigm. Concretely, we design a novel Echo-Based Execution Conclude (EBEC) mechanism to enable minimum redundancy request evaluation while ensure blockchain safety and liveness. Two optimization techniques, Echo Compacting (EC) and Load Balancing (LB), are proposed to reduce the communication and computation overhead of PDPB and further enhance its performance. We implement a prototype of PDPB and evaluate it on Amazon Web Services (AWS) servers. The results show that PDPB achieves more than 35.6% performance improvement over existing methods.
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