Future Generation Computer Systems-The International Journal of Escience最新文献

{"title":"Solving online resource-constrained scheduling for follow-up observation in astronomy: A reinforcement learning approach","authors":"Yajie Zhang,&nbsp;Ce Yu,&nbsp;Chao Sun,&nbsp;Jizeng Wei,&nbsp;Junhan Ju,&nbsp;Shanjiang Tang","doi":"10.1016/j.future.2025.107781","DOIUrl":"10.1016/j.future.2025.107781","url":null,"abstract":"<div><div>In the astronomical observation field, determining the allocation of observation resources of the telescope array and planning follow-up observations for targets of opportunity (ToOs) are indispensable components of astronomical scientific discovery. This problem is computationally challenging, given the online observation setting and the abundance of time-varying factors that can affect whether an observation can be conducted. This paper presents <span>ROARS</span>, a reinforcement learning approach for online astronomical resource-constrained scheduling. To capture the structure of the astronomical observation scheduling, we depict every schedule using a directed acyclic graph (DAG), illustrating the dependency of timing between different observation tasks within the schedule. Deep reinforcement learning is used to learn a policy that can improve the feasible solution by iteratively local rewriting until convergence. It can solve the challenge of obtaining a complete solution directly from scratch in astronomical observation scenarios, due to the high computational complexity resulting from numerous spatial and temporal constraints. A simulation environment is developed based on real-world scenarios for experiments, to evaluate the effectiveness of our proposed scheduling approach. The experimental results show that <span>ROARS</span> surpasses 5 popular heuristics, adapts to various observation scenarios and learns effective strategies with hindsight.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107781"},"PeriodicalIF":6.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563730","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}

{"title":"An incomplete three-way consensus algorithm for unmanned aerial vehicle purchase using optimization-driven sentiment analysis","authors":"Chao Zhang ,&nbsp;Yating Wang ,&nbsp;Arun Kumar Sangaiah ,&nbsp;Mohammed J.F. Alenazi ,&nbsp;Majed Aborokbah","doi":"10.1016/j.future.2025.107761","DOIUrl":"10.1016/j.future.2025.107761","url":null,"abstract":"<div><div>As a novel productive force in the low-altitude economy, the unmanned aerial vehicle (UAV) industry has emerged as a crucial engine of the digital economy growth. However, high-dimensional online reviews, incomplete information systems (IISs), and coordination among numerous sellers may influence the purchasing decision for UAVs. To address these challenges, first, the sentiment analysis (SA) of UAV online reviews is conducted using BiLSTM and BiGRU models optimized by the hippopotamus optimization (HO) algorithm. Meanwhile, the K-nearest neighbor (KNN) algorithm that combines the Jensen–shannon (JS) divergence with the Hellinger distance is applied to construct a complete information system (CIS). Second, three-way clustering (TWC) is performed on sellers, followed by the calculation of seller weights and group weights using the full consistency method. Third, to closely align with the behavior of sellers, a two-stage consensus reaching process (CRP) model based on TWC and the dual fine-tuning (DFT) theory is proposed, referred to as TWC-DFT-CCRP. In the first stage, the behavior of sellers is adjusted based on the TWC result. In the second stage, optimization-based rules are used to reduce the conflict degree among sellers to reach consensus. Fourth, integrating the TWD process with prospect regret theory (P-RT) can reduce potential decision risks and identify the optimal solution. Finally, the model’s feasibility is demonstrated via a case study of UAV online reviews. In summary, the method not only addresses the challenge of handling high-dimensional data but also optimizes large-scale group decision-making (LSGDM), thereby providing effective decision support for purchasing UAVs.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107761"},"PeriodicalIF":6.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474372","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}

{"title":"Multigrain: Adaptive multilevel hot data identifier with a stack distance-based prefilter","authors":"Hyerim Lee ,&nbsp;Dongchul Park","doi":"10.1016/j.future.2025.107762","DOIUrl":"10.1016/j.future.2025.107762","url":null,"abstract":"<div><div>Many computer system applications, such as data caching and Not AND (NAND) flash memory-based storage systems, employ a hot data identification scheme. However, regardless of the workload characteristics, most existing studies have adopted only a fine-grained (i.e., block-level) hot data decision policy, causing high computational overhead and error rates. Different workloads mandate different treatments to achieve effective hot data identification. Based on our comprehensive workload studies, this paper proposes Multigrain, an <em>adaptive multilevel</em> hot data identification scheme that dynamically selects a coarse-grained (i.e., subrequest-level) policy or coarser-grained (i.e., request-level) policy based on the workload. The proposed Multigrain employs multiple effective bloom filters to capture frequency and recency information. Moreover, it adopts a simple and smart <em>prefilter mechanism</em> leveraging workload stack distance information. To our knowledge, the proposed scheme is the <em>first multilevel coarse-grained hot data identification scheme</em> that judiciously selects an optimal hot data decision granularity to achieve effective and accurate identification. Our extensive experiments with many realistic workloads demonstrate that our adaptive multilevel scheme significantly reduces the execution time (by an average of up to 6.9<span><math><mo>×</mo></math></span>) and error rate (by an average of up to 2.27<span><math><mo>×</mo></math></span>) using the effective coarse-grained policies and a prefiltering mechanism.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107762"},"PeriodicalIF":6.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"ESDI: An efficient and secure data integrity verification scheme for indoor navigation","authors":"Mingxi Liu ,&nbsp;Tailong Yang ,&nbsp;Wenbo Shi ,&nbsp;Athanasios V. Vasilakos ,&nbsp;Ning Lu","doi":"10.1016/j.future.2025.107759","DOIUrl":"10.1016/j.future.2025.107759","url":null,"abstract":"<div><div>Currently, indoor navigation software is typically embedded in smartphones as mobile applications. These apps enable users to access cloud-based data while retrieving indoor navigation information. However, cloud data faces risks of tampering and deletion, necessitating verification of its integrity by users. While smartphones possess certain computational capabilities, prolonged execution of computationally intensive tasks can lead to rapid battery depletion. Additionally, excessive storage demands may prompt users to frequently close or even uninstall the apps to free up memory. This paper presents a blockchain-based data integrity verification scheme tailored for indoor navigation. To address storage overhead, we introduce a user-frequency-based selection technique that designates certain blockchain nodes as light nodes. We further propose a Merkle Hash Tree-based proof extraction method to facilitate efficient proof transfer between different types of nodes. Our approach incorporates an efficient Zhang-Safavi-Susilo (ZSS) signature-based data auditing protocol. By leveraging a data label placement mechanism during signature generation, our scheme supports tamper-proof batch verification, significantly reducing computational overhead. To enable dynamic data updates, we design a novel dynamic data structure, the Red-Black Hash Table, which enhances efficiency in handling updates. Through rigorous security analysis, we demonstrate that our scheme effectively defends against forgery, replay, and replacement attacks. We implemented and simulated our solution on smartphones and indoor navigation apps, conducting experimental evaluations using indoor positioning data. We take audit initialization overhead, audit verification computation overhead, evidence storage overhead, consensus computation overhead, etc. as important experimental indicators. Performance results indicate that our scheme, Efficient and Secure Data Integrity (ESDI), improves auditing efficiency by approximately 54% on average compared to existing approaches.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107759"},"PeriodicalIF":6.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519546","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}

{"title":"Energy-aware scheduling for reliability-oriented real-time parallel applications allocation on heterogeneous computing systems","authors":"Rui She ,&nbsp;Yuting Wu ,&nbsp;Enfang Cui","doi":"10.1016/j.future.2025.107738","DOIUrl":"10.1016/j.future.2025.107738","url":null,"abstract":"<div><div>Heterogeneous computing systems (HCSs) have rapidly developed and been widely applied due to their high performance and low cost characteristics. However, HCSs face trade-offs and conflicts among the three core indicators: energy consumption, reliability, and scheduling length. How to balance the three core indicators to achieve optimal performance is the core issue faced by HCSs. In this paper, we propose an energy-aware scheduling model for reliability-oriented real-time parallel applications on heterogeneous computing systems. The problem of minimum system-centric energy efficiency problem is studied. In terms of problem solving, minimum schedule time length (MSTL) algorithm is proposed, which provides a baseline for assessing feasibility and ensuring compliance with both response time and reliability criteria. To further enhance reliability, this paper considers both transient faults and permanent faults, and proposes the primary–secondary backup (PSB) algorithm to improve the fault tolerance, with dynamic power management (DPM) and dynamic voltage and frequency scaling (DVFS) to reduce energy consumption. Furthermore, the dynamic voltage and frequency scaling (DVFS) algorithm is proposed, within the deadline, redistributing tasks that have not been executed on failed processors to reduce energy consumption caused by excessively long redundant backups. Extensive experimental results on real-world and randomly generated applications demonstrate the effectiveness of the proposed algorithms under various conditions.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107738"},"PeriodicalIF":6.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480287","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}

{"title":"GAS-MARL: Green-Aware job Scheduling algorithm for HPC clusters based on Multi-Action Deep Reinforcement Learning","authors":"Rui Chen ,&nbsp;Weiwei Lin ,&nbsp;Huikang Huang ,&nbsp;Xiaoying Ye ,&nbsp;Zhiping Peng","doi":"10.1016/j.future.2025.107760","DOIUrl":"10.1016/j.future.2025.107760","url":null,"abstract":"<div><div>In recent years, the computational power of High-Performance Computing (HPC) clusters has surged. However, amidst global calls for energy conservation and emission reduction, their rapid power consumption poses a developmental bottleneck. Adopting renewable energy sources for power supply is a crucial measure to reduce carbon emissions from HPC clusters. However, due to the variability and intermittency of renewable energy, formulating effective job scheduling plans to fully utilize these sources has become urgent. To tackle this, we propose a Green-Aware job Scheduling algorithm for HPC clusters based on Multi-Action Deep Reinforcement Learning (GAS-MARL), which optimizes both renewable energy utilization and average bounded slowdown. In this algorithm, the agent outputs two actions during one decision-making period: job selection action and delay decision action. The introduction of delay decision actions enhances the flexibility of the scheduling algorithm, enabling each job to be executed during appropriate time slots. Furthermore, we have designed a new backfilling policy called Green-Backfilling to better cooperate with GAS-MARL for job scheduling. Experimental evaluations demonstrate that, compared to other algorithms, the combination of GAS-MARL and Green-Backfilling exhibits significant advantages in enhancing renewable energy utilization and decreasing average bounded slowdown.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107760"},"PeriodicalIF":6.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465147","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}

{"title":"Self-sovereign identity framework with user-friendly private key generation and rule table","authors":"Jungwon Seo ,&nbsp;Sooyong Park","doi":"10.1016/j.future.2025.107757","DOIUrl":"10.1016/j.future.2025.107757","url":null,"abstract":"<div><div>The rise of self-sovereign identity (SSI) technology plays a critical role in addressing the limitations of conventional digital identity management systems. This paper focuses on the credential layer within the SSI technology stack, presenting a comprehensive solution to challenges related to usability, inefficient encryption and decryption processes, and verifiable credential management in existing SSI frameworks. To tackle these issues, the proposed approach introduces a user-friendly private key generation method, a rule table-based encryption and decryption technique, and a verifiable credential management system using smart contracts. In a usability evaluation involving 58 participants, 74.1% rated the proposed approach as user-friendly. Performance evaluations demonstrated that the rule table-based encryption method is between 10.37 and 171.51 times faster than existing encryption techniques. Similarly, the decryption process showed significant improvements, achieving performance that is 16.94 to 58.68 times faster than traditional methods. Security analyses were also conducted, highlighting the resilience against brute-force attacks and unauthorized access. The impact of this research extends beyond addressing current limitations, offering a robust and efficient framework that enhances the usability, security, and performance of SSI systems. By advancing the credential layer, this work paves the way for broader adoption of SSI technology across diverse applications, contributing to the evolution of decentralized identity management solutions.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107757"},"PeriodicalIF":6.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"MCMC generation of cost matrices for scheduling performance evaluation","authors":"Louis-Claude Canon,&nbsp;Anthony Dugois,&nbsp;Mohamad El Sayah,&nbsp;Pierre-Cyrille Héam","doi":"10.1016/j.future.2025.107758","DOIUrl":"10.1016/j.future.2025.107758","url":null,"abstract":"<div><div>In high performance computing, scheduling and allocating tasks to machines has long been a critical challenge, especially when dealing with heterogeneous execution costs. To design efficient algorithms and then assess their performance, many approaches have been proposed, among which simulations, which can be performed on a large variety of environments and application models. However, this technique is known to be sensitive to bias when it relies on random instances with an uncontrolled distribution. In this article, instead of designing a new optimization method, we focus on generating cost matrices to improve the empirical evaluation methodology. In particular, we use methods from the literature to provide formal guarantee on how costs matrices are distributed: we ensure a uniform distribution among the cost matrices with given task and machine heterogeneities. Although the use of randomly generated matrices has often been criticized, this new generation procedure is the first that is proven to prevent biased generation by ensuring a uniform generation with given properties. This method is relevant to assess the performance of scheduling heuristics, in particular when characterizing for which parameter values a given approach performs better than others. When applied to a makespan minimization problem, the methodology reveals when each of three efficient heuristics performs better depending on the instance heterogeneity.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107758"},"PeriodicalIF":6.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511793","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}

{"title":"Research on transaction allocation strategy in blockchain state sharding","authors":"Guangxia Xu ,&nbsp;Zhean Zhou ,&nbsp;Xiaoling Song ,&nbsp;Yongfei Huang","doi":"10.1016/j.future.2025.107756","DOIUrl":"10.1016/j.future.2025.107756","url":null,"abstract":"<div><div>With the continuous enrichment of blockchain application scenarios, people have higher requirements for blockchain throughput and storage costs. State sharding is one of the most promising technologies for blockchain. It decentralizes the storage of the blockchain ledger to effectively reduce storage costs while increasing the throughput of the blockchain. However, it still has the hot sharding problem of most transactions in individual committees. This paper proposes a sharding transaction allocation strategy (STAS) to score committees and transactions according to different methods and assign high-scoring transactions to high-scoring committees. This allocation strategy, which allocates transactions on demand based on node capacity, mitigates hot sharding issues and makes it safer to hand over more valuable transactions to a more honest committee. Comparative experiments show that the proposed STAS strategy has lower latency and higher throughput than the previous sharding model.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"168 ","pages":"Article 107756"},"PeriodicalIF":6.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548537","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}

{"title":"Accelerating complex graph queries by summary-based hybrid partitioning for discovering vulnerabilities of distribution equipment","authors":"Qiong Wang ,&nbsp;Wei He ,&nbsp;Shang Yang ,&nbsp;Ruoyu Zhao ,&nbsp;Yinglong Ma","doi":"10.1016/j.future.2025.107747","DOIUrl":"10.1016/j.future.2025.107747","url":null,"abstract":"<div><div>With the high proportion of electrical and electronic devices in China’s power grids, massive graph data of power distribution equipment has been accumulated to share the knowledge across heterogeneous information, while the vulnerabilities of power devices consequently trigger new security risks to the power grid. It is crucial to swiftly and accurately discover the intrinsic vulnerabilities of power devices from the massive power distribution graph data for ensuring safe operation of the power grid. However, diverse complex queries make it inefficient to achieve consistent graph querying performance over the massive power graph data for swift and accurate vulnerability discovery in a highly available and user-friendly manner. To handle the aforementioned problem, in this paper, we present a power graph query-oriented pipeline framework to consistently accelerate complex graph queries over the massive graph data of power distribution equipment for efficient vulnerability discovery. First, we propose a lossless graph summarization method, through which a summary graph is produced from the raw graph data. Second, very different from existing methods, we propose a two-stage hybrid partitioning including the binary partitioning and the consequent ternary partitioning, which is conducted based on the summary graph instead of the raw graph for reducing the search scope and minimizing the input of the queried data, thereby accelerating the query. Third, the complex graph query with multiple triplet patterns will be automatically translated into the Spark SQL statement for query execution without users’ interference, through which the accurate results will be obtained by recovering the summary-based intermediate results. At last, extensive experiments were made over four datasets against some state-of-the-art methods, and the results show that our approach is very competitive with these approaches and achieves consistent graph querying performance in accelerating complex graph queries while obtaining accurate results.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107747"},"PeriodicalIF":6.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419072","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}