Future Generation Computer Systems-The International Journal of Escience最新文献_第4页

Run time dynamic digital twins and dynamic digital twins networks 运行时动态数字孪生和动态数字孪生网络

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-23 DOI: 10.1016/j.future.2025.107823

Alexander Vodyaho , Radhakrishnan Delhibabu , Dmitry I. Ignatov , Nataly Zhukova

{"title":"Run time dynamic digital twins and dynamic digital twins networks","authors":"Alexander Vodyaho , Radhakrishnan Delhibabu , Dmitry I. Ignatov , Nataly Zhukova","doi":"10.1016/j.future.2025.107823","DOIUrl":"10.1016/j.future.2025.107823","url":null,"abstract":"<div><div>Digital twins are widely used for building various types of cyber–physical systems. There are a huge number of publications devoted to the use of digital twins in production systems. Much less attention is paid to the issues of building runtime digital twins. The article describes an approach to building complex distributed cyber–physical systems with a high level of architectural dynamics built on fog and edge computing platforms based on the use of digital twins. The issues of implementing runtime digital twins and distributed systems of runtime digital twins are considered. The requirements to runtime digital twins are defined. Typical problem statements for constructing and maintaining a runtime digital twin system are formulated. A reference architecture of a dynamic runtime digital twin is proposed, which includes a model of the observed system (or the object) and a model processor. The dynamic model of the observed and managed system is considered as a key element of the digital twin. Possible approaches to the synthesis of built-in models of runtime digital twins are discussed. Examples of using the proposed approach to solve practical problems are given. The described approach may be of interest to specialists involved in research and development of various types of information systems implemented on Internet of Things platforms, such as smart cities, smart transport, medical information systems, etc. It is proposed to conduct further research and development in the areas of creating human digital twins.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"172 ","pages":"Article 107823"},"PeriodicalIF":6.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928704","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

SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum SmartKV：用于计算连续体的经济高效且低延迟的地理分布式键值存储

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-19 DOI: 10.1016/j.future.2025.107857

Juan Aznar Poveda , Maximilian Franz Ebner , Thomas Fahringer , Zahra Najafabadi Samani , Marlon Etheredge , Stefan Pedratscher , Nishant Saurabh

{"title":"SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum","authors":"Juan Aznar Poveda , Maximilian Franz Ebner , Thomas Fahringer , Zahra Najafabadi Samani , Marlon Etheredge , Stefan Pedratscher , Nishant Saurabh","doi":"10.1016/j.future.2025.107857","DOIUrl":"10.1016/j.future.2025.107857","url":null,"abstract":"<div><div>Many data-intensive and distributed applications rely on low-latency and scalable key–value storage systems across the Computing Continuum. Key–value storage systems typically use consistent hashing or hash slot-sharding mechanisms to distribute data across storage nodes, which ensures load balancing but often leads to sub-optimal response times and monetary costs, particularly in geo-distributed systems where nodes might have different unit prices and be widely dispersed. In this paper, we propose <span>SmartKV</span>, a cost-efficient geo-distributed key–value store that optimizes data placement dynamically, abstracting the intricacies of data organization, transfer, access, and processing. <span>SmartKV</span> integrates a decentralized data placement algorithm that optimizes the replication factor and selects suitable locations for key–value pairs and replicas, balancing cost and access latency while keeping optimization overhead low. We employ a realistic cost model based on public and private Cloud and Edge providers that consider data transfer, request, and storage costs. In addition to conventional key–value pairs, <span>SmartKV</span> supports active key–value pairs, which enable the definition of custom data types and the execution of user-defined functions directly on the storage side. This contributes to reducing data transfer costs and round-trip times. We thoroughly evaluate <span>SmartKV</span> across different regions of the Chameleon testbed using several realistic workloads. Results show that the utilized decentralized data placement strategy allows <span>SmartKV</span> to reduce round trip times between 9 and 84% while reducing costs up to 4.84<span><math><mo>×</mo></math></span> under different client workloads and consistency models compared to state-of-the-art data placement strategies.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107857"},"PeriodicalIF":6.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878899","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 verifiable query scheme with rich query capabilities and low storage redundancy on blockchain 区块链上具有丰富查询能力和低存储冗余的可验证查询方案

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-19 DOI: 10.1016/j.future.2025.107859

Linkun Sun , Luqi Wang , Wenbao Jiang, Yangnan Guo

{"title":"A verifiable query scheme with rich query capabilities and low storage redundancy on blockchain","authors":"Linkun Sun , Luqi Wang , Wenbao Jiang, Yangnan Guo","doi":"10.1016/j.future.2025.107859","DOIUrl":"10.1016/j.future.2025.107859","url":null,"abstract":"<div><div>In current blockchain verifiable query research, redundant storage of data to be indexed is often required to enable efficient and feature-rich query algorithms. However, most blockchains currently face the problem of rapid data growth, leading to significant storage resource consumption by nodes. To provide a high-efficiency and generic verifiable query capability while reducing the storage burden on nodes, we propose a cryptographic accumulator-based verifiable generic query scheme. By skilfully establishing a logical relationship between the cryptographic accumulation value and the proof of element members, our scheme effectively reduces the redundant design of data structures on the chain. Additionally, we design a filter aggregation algorithm, an inter-block indexing structure, and a range query method that transforms the numerical attribute comparison problem into a proof-of-existence problem for members in a collection based on this scheme. Security analysis and experimental analysis verify the robustness and practicality of the proposed techniques.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107859"},"PeriodicalIF":6.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859486","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 learning for heterogeneous neural networks with layer similarity relations in Cloud–Edge–End scenarios 云-端场景中具有层相似性关系的异构神经网络的联合学习

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-19 DOI: 10.1016/j.future.2025.107856

Rao Fu, Yongqiang Gao, Zijian Qiao

{"title":"Federated learning for heterogeneous neural networks with layer similarity relations in Cloud–Edge–End scenarios","authors":"Rao Fu, Yongqiang Gao, Zijian Qiao","doi":"10.1016/j.future.2025.107856","DOIUrl":"10.1016/j.future.2025.107856","url":null,"abstract":"<div><div>Federated Learning (FL) aims to allow numerous clients to participate in collaborative training in an efficient communication manner without exchanging private data. Traditional FL assumes that all clients have sufficient local resources to train models with the same architecture, and does not consider the reality that clients may struggle to deploy the same model across devices with varying computational resources. To address this, we propose a heterogeneous FL method, HNN-LSFL, in which the edge server first aggregates the clients of the homogeneous model, and then the cloud server selectively aligns and aggregates the knowledge between the heterogeneous models according to the layer similarity. This Cloud–Edge–End tiered architecture effectively utilizes the powerful computing power of cloud servers, reduces the computational cost of multiple alignment and aggregation of heterogeneous models, and reduces the communication cost with the cloud, which is more suitable for large-scale client scenarios. By identifying layer similarities, the method finds commonalities between different models, enabling more valuable aggregations and reducing the transmission of unnecessary parameters. We also evaluated HNN-LSFL on heterogeneous datasets, demonstrating that it not only improves the utilization of local client resources but also optimizes FL performance. By transmitting fewer model parameters, it reduces the risk of privacy leaks and proves to be superior in FL tasks with heterogeneous models compared to current state-of-the-art heterogeneous FL algorithms.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107856"},"PeriodicalIF":6.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874639","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

HalfFedLearn: A secure federated learning with local data partitioning and homomorphic encryption HalfFedLearn：具有本地数据分区和同态加密功能的安全联合学习

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-18 DOI: 10.1016/j.future.2025.107858

Rojalini Tripathy, Jigyasa Meshram, Padmalochan Bera

{"title":"HalfFedLearn: A secure federated learning with local data partitioning and homomorphic encryption","authors":"Rojalini Tripathy, Jigyasa Meshram, Padmalochan Bera","doi":"10.1016/j.future.2025.107858","DOIUrl":"10.1016/j.future.2025.107858","url":null,"abstract":"<div><div>Federated Learning (FL) is an emerging technology in collaborative machine learning, where multiple data owners train a unified model by exchanging model parameters instead of private data. Despite providing data privacy and a wide range of applications, FL faces several challenges, such as slow convergence, high computation and communication costs, and security in parameter sharing. In this paper, we propose HalfFedLearn to address these challenges using Homomorphic Encryption (HE) and local horizontal data partitioning. We leverage the inherent distribution of the dataset to use horizontal data partitioning based on data sensitivity and enforce selective security on private data samples using HE. HalfFedLearn minimizes the data volume per client, which reduces local training time and computation. Also, the number of communication rounds decreases due to the reduction in local dataset size. We experimented on MNIST, CIFAR-10, and FMNIST datasets with varying clients and number of rounds. The results demonstrate that HalfFedLearn increases accuracy by 3%–6% and achieves an average reduction of 29.33% in training rounds, with a maximum training time reduction of 9.94% per round. Additionally, we performed a comparative analysis of the computation, communication cost, and security that shows the efficacy of HalfFedLearn.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107858"},"PeriodicalIF":6.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864738","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

Fluid Computing & Digital Twins for intelligent interoperability in the IoT ecosystem 流体计算和数字孪生在物联网生态系统中的智能互操作性

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-18 DOI: 10.1016/j.future.2025.107855

Luca Bedogni, Marco Mamei, Marco Picone, Marcello Pietri, Franco Zambonelli

{"title":"Fluid Computing & Digital Twins for intelligent interoperability in the IoT ecosystem","authors":"Luca Bedogni, Marco Mamei, Marco Picone, Marcello Pietri, Franco Zambonelli","doi":"10.1016/j.future.2025.107855","DOIUrl":"10.1016/j.future.2025.107855","url":null,"abstract":"<div><div>The integration of physical and digital systems is fundamental to enabling intelligent, adaptive, and scalable solutions in modern IoT environments. This paper explores Fluid Digital Twins (FDTs), a novel framework combining Fluid Computing (FC) principles with Digital Twin (DT) technology, to address challenges related to interoperability, dynamic functionality, and adaptability in IoT ecosystems. FC introduces a paradigm shift, enabling seamless data and computational task flow across heterogeneous environments, dynamically adjusting to resource availability and system needs. This paper focuses on embedding intelligence within FDTs to enhance interoperability and enable IoT applications to adapt to changes across both physical and digital domains. By integrating intelligent interoperability mechanisms, FDTs ensure smooth data alignment and compatibility across platforms, adapting to both physical and digital changes. The proposed framework has been implemented, prototyped, and evaluated in the Modena Automotive Smart Area (MASA), a smart city testbed. The evaluation demonstrates FDTs’ ability to enhance smart mobility, optimize transportation systems, and provide actionable insights, highlighting their transformative potential in dynamic, data-rich environments. The results emphasize the practical applicability of FDTs in addressing real-world challenges and advancing the capabilities of IoT-driven smart cities.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107855"},"PeriodicalIF":6.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869449","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

Next-generation web 3.0 for digitalized industrial applications in the 5G/6G era 5G/6G时代数字化工业应用的下一代web 3.0

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-17 DOI: 10.1016/j.future.2025.107835

Qingqi Pei , F. Richard Yu , Kaou Ota , Mohammed Atiquzzaman , Youshui Lu

{"title":"Next-generation web 3.0 for digitalized industrial applications in the 5G/6G era","authors":"Qingqi Pei , F. Richard Yu , Kaou Ota , Mohammed Atiquzzaman , Youshui Lu","doi":"10.1016/j.future.2025.107835","DOIUrl":"10.1016/j.future.2025.107835","url":null,"abstract":"<div><div>In recent years, the rapid development of 5G/6G networks has connected billions of IoT devices, generating massive amounts of data. Efficiently collecting, processing, and analyzing this data is crucial for gaining valuable insights and improving decision-making. However, the underlying communication networks face significant challenges in securely and scalably managing these devices, impacting infrastructure construction, maintenance, and management. Moreover, recurring data privacy breaches and a lack of control have diminished the willingness of users and enterprises to share data for processing and analysis. Web 3.0, with its emphasis on enhanced security and decentralized data management, offers robust solutions to protect sensitive industrial information. Its technologies enable transparent and tamper-resistant data exchanges, optimizing supply chain and asset management. Smart contracts boost operational efficiency, while self-sovereign identity systems ensure privacy compliance. Being compatible with 5G/6G networks, Web 3.0 seamlessly integrates with advanced communication infrastructures, promoting efficient data processing, communication, and resource management. The applications of Web 3.0 span across the Internet of Things (IoT), federated learning, and digital twins, all aiming to enhance data processing efficiency, security, privacy protection, and resource management optimization. This transformative technology is driving progress and innovation across various sectors, including industry, transportation, and data management, paving the way for a more connected and intelligent future. The special issue concludes with 18 scientific papers after a rigorous peer-review process.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"173 ","pages":"Article 107835"},"PeriodicalIF":6.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098512","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

DFASCN:A distributed flocking approach for UAV swarm collective navigation DFASCN：一种无人机群体集体导航的分布式群集方法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-15 DOI: 10.1016/j.future.2025.107852

Yibing Li , Zitang Zhang , Yujie Huang , Zongyu He , Qian Sun , Qianhui Dong

{"title":"DFASCN:A distributed flocking approach for UAV swarm collective navigation","authors":"Yibing Li , Zitang Zhang , Yujie Huang , Zongyu He , Qian Sun , Qianhui Dong","doi":"10.1016/j.future.2025.107852","DOIUrl":"10.1016/j.future.2025.107852","url":null,"abstract":"<div><div>In recent years, the application domains of unmanned swarms have been continuously expanding. Existing swarm navigation methods predominantly rely on communication networks for frequently information exchange to achieve stable navigation behavior. However, this reliance presents challenges in achieving coordinated cooperative behavior in communication-restricted and obstacle-rich environments. To ensure the task efficiency of swarms in such mission settings, we propose a distributed flocking framework to guide unmanned aerial vehicle (UAV) swarms in navigating from a starting point to a target in unknown environments.Our approach begins by employing Boyd’s OODA loop (Observe, Orient, Decide, Act), combined with a locally limited perception model, to develop an interactive decision-making process between individual UAVs and their external environment. We classify the roles of different UAV platforms within the swarm, enhancing cooperative flight efficiency through the guiding behavior of critical nodes. Each UAV utilizes a dynamic adjustment mechanism for control parameters, allowing adaptive modifications based on local flight states. Additionally, each UAV is equipped with a model predictive control (MPC) controller, which provides feasible control inputs to ensure robust and reliable operation in complex and dynamic scenarios.To evaluate the adaptability of our method, we conducted simulations across various task environments with differing obstacle densities and numbers of UAVs. The results validate the algorithm’s effectiveness and scalability, highlighting its robustness and potential applicability to real-world scenarios.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107852"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838901","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

Rethinking federated learning as a digital platform for dynamic and value-driven participation 重新思考联邦学习作为动态和价值驱动参与的数字平台

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-12 DOI: 10.1016/j.future.2025.107847

Christoph Düsing, Philipp Cimiano

{"title":"Rethinking federated learning as a digital platform for dynamic and value-driven participation","authors":"Christoph Düsing, Philipp Cimiano","doi":"10.1016/j.future.2025.107847","DOIUrl":"10.1016/j.future.2025.107847","url":null,"abstract":"<div><div><em>Federated learning</em> (FL) has emerged as a powerful framework for privacy-preserving machine learning, especially relevant in fields like healthcare, finance, and mobile devices. Despite its success, traditional FL systems have a significant limitation: they rely on a static set of clients, forming a federation at the beginning of the training process, which remains fixed throughout the training cycle, thus limiting their scalability and adaptability in dynamic, data-rich settings. To address this, we introduce the concept of <em>federated learning platforms</em> (FLPs), which extend FL into a dynamic platform where client participation is continuously adapted based on their expected value and strategic incentives. In this paper, we envision FLPs as a natural extension of conventional FL that resemble dynamic, value-driven digital platforms where participants can join or leave the federation at any time. Given this dynamicity of client participation, FLPs are designed to gracefully handle changes in the client pool to uphold their value proposition. In this article, we propose a framework for implementing FLPs, outlining key components such as those for dynamic FLP governance, including client on- and offboarding as well as process monitoring. Furthermore, we demonstrate the practical viability of FLPs through a proof of concept for an exemplary use-case and discuss key challenges related to federation stability, data interoperability, as well as privacy, alongside potential solutions. Finally, we present a roadmap and future research directions, guiding the development of robust and scalable FLPs to drive innovation in FL and data interoperability.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107847"},"PeriodicalIF":6.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838903","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 profit maximization in reliability concerned static vehicular cloud system 静态车辆云系统可靠性的有效利润最大化

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-12 DOI: 10.1016/j.future.2025.107850

Suvarthi Sarkar , Akshat Arun , Harshit Sureka , Aryabartta Sahu

{"title":"Efficient profit maximization in reliability concerned static vehicular cloud system","authors":"Suvarthi Sarkar , Akshat Arun , Harshit Sureka , Aryabartta Sahu","doi":"10.1016/j.future.2025.107850","DOIUrl":"10.1016/j.future.2025.107850","url":null,"abstract":"<div><div>Modern vehicles are equipped with high-performance compute systems. These compute resources mostly stay idle as most of the time vehicles get parked in the parking lots. In this work, we propose to utilize the unused compute resources of the vehicles efficiently to enhance the computing power of regular cloud systems, which is termed as vehicular cloud. Unlike in traditional cloud computing resources, the vehicles or vehicular compute resources move in or out of the parking lot, which introduces dynamic nature of the available compute resources. This makes it challenging for the vehicular cloud to ensure reliability of execution of the user-submitted tasks.</div><div>In this work, we propose an approach to maximize the profit of the vehicular cloud by ensuring the reliability of the vehicular cloud. We consider user-submitted tasks with execution time, deadline and revenue associated with it. Our approach classifies the tasks based on the deadline, and orders the tasks for task admission based on the expected profit of the task. We also perform the classification of available vehicular units based on the expected residency time of vehicles and use the same for allocating vehicular units for redundant execution of task to ensure higher reliability. As the task execution time has a direct impact on redundancy requirements to ensure higher reliability, we convert the longer tasks to a chain of shorter sub-tasks to reduce the redundancy requirement. Our experiments show that the proposed approach outperforms the state-of-the-art approach with a profit margin increasing up to 25 to 45 % in real-life scenarios.The codes and dataset for this work are available at our <span><span>https://github.com/SuvarthiSarkar/Efficient-profit-maximization-in-reliability-concerned-static-vehicular-cloud-system.git</span><svg><path></path></svg></span>GitHub repository.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107850"},"PeriodicalIF":6.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847726","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