Future Generation Computer Systems-The International Journal of Escience最新文献_第3页

Delay-cost computation offloading for on-board emergency tasks in LEO Satellite Edge Computing networks

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-15 DOI: 10.1016/j.future.2025.107797

Changhao Li , Zhenmou Liu , Zhicong Ye , Guoguang Wen , Zong-Fu Luo , Chuanfu Zhang

{"title":"Delay-cost computation offloading for on-board emergency tasks in LEO Satellite Edge Computing networks","authors":"Changhao Li , Zhenmou Liu , Zhicong Ye , Guoguang Wen , Zong-Fu Luo , Chuanfu Zhang","doi":"10.1016/j.future.2025.107797","DOIUrl":"10.1016/j.future.2025.107797","url":null,"abstract":"<div><div>The increasing computational capabilities of Low Earth Orbit (LEO) constellations have significantly augmented their autonomy and operational flexibility. Complex onboard tasks such as observation, sensing, and situational awareness can be processed and executed directly on the Satellite Edge Computing (SEC) networks. Due to the time-varying characteristics of inter-satellite links and the uncertainty in the load of edge satellites, efficient offloading of on-board tasks presents significant challenges. We introduce an on-board distributed task offloading method for LEO satellite tasks in emergency to enhance service quality. We initially design a dynamic offloading scheme, in which data-source satellites can transmit tasks to edge nodes. Then, we formulate the multi-hop satellite network dynamic offloading (MSNDO) problem to minimize system delay and maximize success ratio of time-sensitive tasks under multiple constraints. Finally, we propose a distributed deep reinforcement learning algorithm that allows individual satellites to design offloading strategies without knowing the decision-making patterns of other satellites. Simulation experiments show that the proposed algorithm can utilize the edge satellite processing capabilities more efficiently and significantly improve the performance of the SEC system.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107797"},"PeriodicalIF":6.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677754","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}

引用次数: 0

DrugPred: An ensemble learning model based on ESM2 for predicting potential druggable proteins

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-15 DOI: 10.1016/j.future.2025.107801

Hong-Qi Zhang , Shang-Hua Liu , Jun-Wen Yu , Rui Li , Dong-Xin Ye , Yan-Ting Jin , Cheng-Bing Huang , Ke-Jun Deng

{"title":"DrugPred: An ensemble learning model based on ESM2 for predicting potential druggable proteins","authors":"Hong-Qi Zhang , Shang-Hua Liu , Jun-Wen Yu , Rui Li , Dong-Xin Ye , Yan-Ting Jin , Cheng-Bing Huang , Ke-Jun Deng","doi":"10.1016/j.future.2025.107801","DOIUrl":"10.1016/j.future.2025.107801","url":null,"abstract":"<div><div>The Human Genome Project has generated abundant data for a long time, but transforming this data into practical and usable drug or drug target products remains challenging. This study proposed an ensemble learning model, called DrugPred, to achieve prediction of drug targets using evolutionary scale modeling (ESM2) and amino acid composition (AAC) as features. ESM2 utilized deep learning technology to study the sequence-structure-function relationship of protein sequences, extracting highly abstract features of proteins. AAC translated protein sequences into amino acid percentages, reflecting the composition of amino acids in proteins. The integration of two features constituted a multidimensional and diverse feature space, enabling the model to perform well in predicting drug targets. We input the fused features into four machine learning algorithms for separate training and generated the prediction probabilities, then input them into a support vector machine for voting decisions. This ensemble learning overcame the bias of a single algorithm model in information learning and improved the stability and accuracy of the model. After comprehensive evaluation, the model achieved an accuracy of 0.9691 with an area under the receiver operating characteristic curve (AUC) value of 0.9868. We also used t-distributed Stochastic Neighbor Embedding (t-SNE) and SHapley Additive exPlanations (SHAP) techniques to explore the interpretability of the DrugPred model. This study provided a fresh perspective and method for identifying drug targets, offering robust support for future drug development. We have developed and made publicly accessible a web server based on the DrugPred model. The web server is accessible at <span><span>http://drugpred.lin-group.cn/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"170 ","pages":"Article 107801"},"PeriodicalIF":6.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704949","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}

引用次数: 0

Efficient memory management with co-timeout policy eviction and history-enlightened selective strategy installation

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-12 DOI: 10.1016/j.future.2025.107799

Xianfeng Li , Haisheng Yu , Xue Yang , Haoran Sun , Yan Huang

{"title":"Efficient memory management with co-timeout policy eviction and history-enlightened selective strategy installation","authors":"Xianfeng Li , Haisheng Yu , Xue Yang , Haoran Sun , Yan Huang","doi":"10.1016/j.future.2025.107799","DOIUrl":"10.1016/j.future.2025.107799","url":null,"abstract":"<div><div>In Software-Defined Network (SDN), the controller plays a crucial role in implementing fine-grained network policies by installing flow rules in the switch’s flow table. However, the limited capacity of the flow table, often implemented using TCAM, poses scalability challenges due to its low density and high energy consumption. To address this issue, this paper focuses on two key aspects: (1) early eviction of installed flow rules and (2) selective installation of flow rules.</div><div>To tackle the first aspect, we propose an adaptive timeout mechanism called Two-Stage Timeout (TST) that enhances the flow table architecture. TST enables the efficient eviction of short-lived flows, creating space for more valuable flow rules. In addition, the introduction of the Inactive Flow Queue (IFQ) improves the retention of active flows, enhancing overall table management.</div><div>For the second aspect, we introduce RICHRIN, a mechanism that combines historical and real-time information to prevent the installation of unnecessary flow rules that contribute little to cache hit rates. RICHRIN effectively filters out a significant portion of these unproductive flows, reducing pollution in the flow table.</div><div>To evaluate the performance of TST and RICHRIN, we conduct experiments using real network packet traces from CAIDA. The results demonstrate that these mechanisms significantly improve the rule cache hit ratio and substantially reduce the number of rule installations. This research effectively addresses the scalability problem of the flow table and provides valuable insights for optimizing flow table management in SDN environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107799"},"PeriodicalIF":6.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681392","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}

引用次数: 0

Effect of implementations of the N-body problem on the performance and portability across GPU vendors

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-12 DOI: 10.1016/j.future.2025.107802

Rodrigo A.C. Bartolomeu , René Halver , Jan H. Meinke , Godehard Sutmann

引用次数: 0

Keyed watermarks: A fine-grained watermark generation for Apache Flink

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-11 DOI: 10.1016/j.future.2025.107796

Tawfik Yasser , Tamer Arafa , Mohamed ElHelw , Ahmed Awad

{"title":"Keyed watermarks: A fine-grained watermark generation for Apache Flink","authors":"Tawfik Yasser , Tamer Arafa , Mohamed ElHelw , Ahmed Awad","doi":"10.1016/j.future.2025.107796","DOIUrl":"10.1016/j.future.2025.107796","url":null,"abstract":"<div><div>Big Data Stream processing engines, exemplified by tools like Apache Flink, employ windowing techniques to manage unbounded streams of events. Aggregating relevant data within Windows is important for event-time windowing due to its impact on result accuracy. A pivotal role in this process is attributed to watermarks, unique timestamps signifying event progression in time. Nonetheless, the existing watermark generation method within Apache Flink, operating at the input stream level, exhibits a bias towards faster sub-streams, causing the omission of events from slower counterparts. Our analysis determined that Apache Flink’s standard watermark generation approach results in an approximate 33% data loss when 50% of median-proximate keys experience delays. Furthermore, this loss exceeds 37% in cases where 50% of randomly selected keys encounter delays. In this paper, we introduce a pioneering approach termed <em>keyed watermarks</em> to address data loss concerns and enhance data processing precision to a minimum of 99% in most scenarios. Our strategy facilitates distinct progress monitoring by creating individualized watermarks for each sub-stream (key). Within our investigation, we delineate the essential architectural and API modifications requisite for integrating keyed watermarks while also highlighting our experience in navigating the expansion of Apache Flink’s extensive codebase. Moreover, we conduct a comparative evaluation between the efficacy of our approach and the conventional watermark generation technique concerning the accuracy of event-time tracking, the latency of watermark processing, and the growth of Flink’s maintained state.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107796"},"PeriodicalIF":6.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620560","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}

引用次数: 0

A self-organized MoE framework for distributed federated learning

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-11 DOI: 10.1016/j.future.2025.107798

Jungjae Lee, Wooseong Kim

{"title":"A self-organized MoE framework for distributed federated learning","authors":"Jungjae Lee, Wooseong Kim","doi":"10.1016/j.future.2025.107798","DOIUrl":"10.1016/j.future.2025.107798","url":null,"abstract":"<div><div>Federated Learning (FL) has solved the problem of data silos by enabling multiple participants to cooperatively train a global model while ensuring data privacy; however, it is still a challenge to establish a Distributed Federated Learning (DFL) framework that naturally suffers from the heterogeneity of devices and datasets. Rather than conventional FL algorithms that combine client models for a single global model, a Mixture of Experts (MoE) based FL is an effective alternative that can admit individual features on each client dataset by partitioning the entire latent space. In this study, we introduce the Self-Organized MoE Framework (SOMFed), which enhances the DFL lifecycle under asynchronous updates and statistical challenges of datasets. Considering that nodes are assumed to lack label information in contrast to most of previous studies, aside from their class data, we propose the Model Assessment and Selection (MASS) algorithm for the SOMFed framework, leveraging self-supervised learning. It evaluates and chooses suitable experts for own unlabeled dataset by differentiating the performance of the representation layers among experts using Bayesian optimization and Conditional Loss Adjustment (CLA). The SOMFed exhibits superior performance in extensive experiments with different non-IID distributions and stragglers compared to FedAVG, FedAsync, SCAFFOLD, FedAT, and Adaptive Expert Models (AEM). In particular, it demonstrates robustness against pathological non-IID distribution on CIFAR10, achieving accuracy of 79.42%.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107798"},"PeriodicalIF":6.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620557","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}

引用次数: 0

Fast and Privacy-Preserving Spatial Keyword Authorization Query with access control

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-10 DOI: 10.1016/j.future.2025.107774

Bohai Wen, Shengzhou Hu, Xinquan Ma, Huofeng Jia, Longjian Huang

{"title":"Fast and Privacy-Preserving Spatial Keyword Authorization Query with access control","authors":"Bohai Wen, Shengzhou Hu, Xinquan Ma, Huofeng Jia, Longjian Huang","doi":"10.1016/j.future.2025.107774","DOIUrl":"10.1016/j.future.2025.107774","url":null,"abstract":"<div><div>In light of the accelerated advancement of GPS and the explosive growth of data, outsourcing spatial data to cloud servers has become a common practice for location-based service providers to alleviate computational and storage burdens. However, existing spatial keyword query schemes with fine-grained access control often rely on additional encryption techniques, such as homomorphic encryption and RSA, for spatial range queries, resulting in significant computational overhead. Furthermore, most schemes enforce access policies on all index tree nodes, which compromises efficiency and practicality. To address these challenges, we propose the <u>F</u>ast and <u>P</u>rivacy-Preserving Spatial Keyword <u>A</u>uthorization <u>Q</u>uery (FPAQ) scheme. FPAQ leverages Geohash and Quadtree to construct an index tree, achieving sub-linear search complexity and efficient spatial keyword queries. And introduces a novel authorization mechanism based on secret keys, embedding authorization information in non-leaf nodes to minimize computational overhead, while access policies are enforced only on leaf nodes. Additionally, attribute-based encryption is employed to support fine-grained access control in multi-user scenarios. Formal security analysis confirms that FPAQ safeguards data confidentiality and query privacy. Experimental results on the Yelp dataset validate the scheme’s superior efficiency and scalability compared to existing methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107774"},"PeriodicalIF":6.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609985","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}

引用次数: 0

Performance and efficiency: A multi-generational benchmark of modern processors on bandwidth-bound HPC applications

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-06 DOI: 10.1016/j.future.2025.107793

Balázs Drávai, István Z. Reguly

引用次数: 0

zCeph: Design and implementation of a ZNS-friendly distributed file system

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-05 DOI: 10.1016/j.future.2025.107763

Jin Yong Ha , Yongseok Son

{"title":"zCeph: Design and implementation of a ZNS-friendly distributed file system","authors":"Jin Yong Ha , Yongseok Son","doi":"10.1016/j.future.2025.107763","DOIUrl":"10.1016/j.future.2025.107763","url":null,"abstract":"<div><div>This article presents <span>zCeph</span>, a ZNS-friendly distributed file system designed to efficiently utilize zoned namespace (ZNS) SSDs. Specifically, we first propose <span>MZAllocator</span> which enables multiple zones to be utilized simultaneously to maximize the performance of ZNS SSDs. Second, we adopt an <span>append</span> command to eliminate the need for synchronization in write ordering within distributed storage systems to improve scalability. Third, we present <span>zBlueFS</span>, a ZNS-aware user-level file system based on BlueFS to update the metadata on the ZNS SSD without a conventional SSD. Finally, we propose a delta write technique, <span>DeltaWriter</span>, which writes only a modified part of the metadata (i.e., onode) to reduce read–modify–write overhead whenever the metadata are updated. We implement <span>zCeph</span> with four techniques based on Ceph, an open-source distributed file system. Further, we evaluate <span>zCeph</span> on a pair of 48-core machines with ZNS SSDs using micro and macro benchmarks, and the results reveal that <span>zCeph</span> improves performance by up to 4.2<span><math><mo>×</mo></math></span> and 8.8<span><math><mo>×</mo></math></span> compared with Ceph, respectively.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107763"},"PeriodicalIF":6.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592145","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}

引用次数: 0

Federated adaptive pruning with differential privacy

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-05 DOI: 10.1016/j.future.2025.107783

Zhousheng Wang , Jiahe Shen , Hua Dai , Jian Xu , Geng Yang , Hao Zhou

{"title":"Federated adaptive pruning with differential privacy","authors":"Zhousheng Wang , Jiahe Shen , Hua Dai , Jian Xu , Geng Yang , Hao Zhou","doi":"10.1016/j.future.2025.107783","DOIUrl":"10.1016/j.future.2025.107783","url":null,"abstract":"<div><div>Federated Learning (FL), as an emerging distributed machine learning technique, reduces the computational burden on the central server through decentralization, while ensuring data privacy. It typically requires client sampling and local training for each iteration, followed by aggregation of the model on a central server. Although this distributed learning approach has positive implications for the preservation of privacy, it also increases the computational load of local clients. Therefore, lightweight efficient schemes become an indispensable tool to help reduce communication and computational costs in FL. In addition, due to the risk of model stealing attacks when uploaded, it is urgent to improve the level of privacy protection further. In this paper, we propose Federated Adaptive Pruning (FAP), a lightweight method that integrates FL with adaptive pruning by adjusting explicit regularization. We keep the model unchanged, but instead try to dynamically prune the data from large datasets during the training process to reduce the computational costs and enhance privacy protection. In each round of training, selected clients train with their local data and prune a portion of the data before uploading the model for server-side aggregation. The remaining data are reserved for subsequent computations. With this approach, selected clients can quickly refine their data at the beginning of training. In addition, we combine FAP with differential privacy to further strengthen data privacy. Through comprehensive experiments, we demonstrate the performance of FAP on different datasets with basic models, <em>e.g.</em>, CNN, and MLP, just to mention a few. Numerous experimental results show that our method is able to significantly prune the datasets to reduce computational overhead with minimal loss of accuracy. Compared to previous methods, we can obtain the lowest training error, and further improve the data privacy of client-side.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107783"},"PeriodicalIF":6.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563731","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}

引用次数: 0