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

{"title":"Blockchain and digital twin empowered edge caching for D2D wireless networks","authors":"Jianbo Du ,&nbsp;Zuting Yu ,&nbsp;Shulei Li ,&nbsp;Bintao Hu ,&nbsp;Yuan Gao ,&nbsp;Xiaoli Chu","doi":"10.1016/j.future.2024.107704","DOIUrl":"10.1016/j.future.2024.107704","url":null,"abstract":"<div><div>Edge caching is considered a promising technology to fulfill user equipment (UE) requirements for content services. In this paper, we explore the use of blockchain and digital twin technologies to support edge caching in a Device-to-Device (D2D) wireless network, where each UE may fetch content from its own caching buffer, from other UEs through D2D links, or from a content server. A digital twin monitors and predicts the operating status of UE by storing crucial data such as the location, estimated processing capability, and remaining energy of each UE. To enable secure and credible trading between UEs, the blockchain technology is used to supervise transactions and constantly update UEs’ reputation values. We formulate an optimization problem to maximize an objective function that considers the content fetching performance, network lifetime and UE’s handover costs by optimizing the content placement and fetching strategies, subject to constraints on the UE’s storage capacity, the upper limit of serving other UEs, and latency requirements. To solve this complicated problem for a dynamic network environment, we propose a proximal policy optimization-based deep reinforcement learning framework. Simulation results demonstrate that our proposed algorithm converges rapidly and can efficiently maximize the rewards, network lifetime and content fetching gain while minimizing handover costs.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107704"},"PeriodicalIF":6.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166795","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":"Blockchain and timely auction mechanism-based spectrum management","authors":"Hongyi Zhang ,&nbsp;Mingqian Liu ,&nbsp;Yunfei Chen ,&nbsp;Nan Zhao","doi":"10.1016/j.future.2024.107703","DOIUrl":"10.1016/j.future.2024.107703","url":null,"abstract":"<div><div>The rapid development of 5G/B5G communication networks and the exponential growth of next-generation wireless devices require more advanced and dynamic spectrum management and control architecture. Dynamic spectrum management and control based on blockchain is efficient and robust, but the cost of traditional consensus mechanisms is too high. In this paper, we propose a new spectrum management and control architecture based on blockchain and deep reinforcement learning, which proposes a new energy-saving consensus mechanism called proof of hierarchy to encourage blockchain users to perform spectrum sensing and detect spectrum violations. Meanwhile, we propose a timely auction mechanism based on deep reinforcement learning for dynamic spectrum management, achieving secure, efficient, and dynamic allocation of spectrum resources. Through intelligent resource allocation and trusted transaction mechanism, efficient spectrum management is realized to improve spectrum utilization and alleviate the shortage of spectrum resources. The simulation verifies the effectiveness of the proposed architecture. We construct a spectrum management scenario and compare it with the traditional spectrum management method. The experimental results show that the proposed architecture can allocate spectrum resources more efficiently and provide a better user experience.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107703"},"PeriodicalIF":6.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968056","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 and two-tier coordinated load balancing for streaming applications in apache flink","authors":"Hongjian Li,&nbsp;Junlin Li,&nbsp;Xiaolin Duan,&nbsp;Jianglin Xia","doi":"10.1016/j.future.2024.107681","DOIUrl":"10.1016/j.future.2024.107681","url":null,"abstract":"<div><div>Apache Flink has become one of the highly regarded streaming computing frameworks with its excellent advantages of high throughput, low latency, and high reliability. However, the default task scheduling policy follows the first-come-first-served principle, which fails to fully consider the differences in energy efficiency and resource loading of nodes in heterogeneous clusters and may lead to high energy consumption and uneven load distribution when executing jobs. To solve this problem, this paper proposes a two-tier coordinated load balancing and energy-saving scheduling optimization strategy. First, we construct an energy efficiency model based on Service Level Agreements (SLA) and design an Energy-Saving Scheduling Algorithm (ESSA) based on this model, aiming to reduce the energy consumption of Flink clusters when executing jobs. This ESSA algorithm integrally considers the effects of two SLA performance metrics including node response time and throughput on node energy consumption, as well as the differences in the energy efficiencies of different nodes in heterogeneous clusters. Second, in order to solve the load imbalance problem that may be caused by Flink’s default scheduling policy, an Energy-Aware Two-Tier Coordinated Load Balancing algorithm (TTCLB-EA) is proposed, which optimizes the cluster load at both the inter-node and intra-node levels through task based on energy efficiency priorities. Experimental results show that compared with the default scheduling strategy, round-robin scheduling strategy, and St-Stream, the proposed algorithm improves about 14.59%, 12.75%, and 7.32% in load balancing, while saving about 14.52%, 10.54%, and 7.58% in energy consumption, respectively. The proposed algorithms not only enhance the performance of the Flink cluster but also help to reduce energy consumption and achieve more efficient resource utilization.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107681"},"PeriodicalIF":6.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929300","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":"Multi-Tree Genetic Programming with Elite Recombination for dynamic task scheduling of satellite edge computing","authors":"Changzhen Zhang,&nbsp;Jun Yang","doi":"10.1016/j.future.2024.107700","DOIUrl":"10.1016/j.future.2024.107700","url":null,"abstract":"<div><div>Satellite Edge Computing (SEC) can provide task computation services to terrestrial users, particularly in areas lacking terrestrial network coverage. With the increasing frequency of computational demands from Internet of Things (IoT) devices and the limited and dynamic nature of computational resources in Low Earth Orbit (LEO) satellites, making effective real-time scheduling decisions in dynamic environments to ensure high task success rate is a critical challenge. In this work, we investigate the dynamic task scheduling of SEC based on Genetic Programming Hyper-Heuristic (GPHH). Firstly, a new problem model for the dynamic task scheduling of SEC is proposed with the objective of improving the task success rate, where the real-world situations (limited and dynamic nature of satellite resources, randomness and difference of tasks) are taken into account. Secondly, to make efficient real-time routing decision and queuing decision during the dynamic scheduling process, a novel scheduling heuristic with routing rule and queuing rule is developed, considering dynamic features of the SEC system such as real-time load, energy consumption, and remaining deadlines. Thirdly, to automatically learn both routing rule and queuing rule, and improve the performance of the algorithm, a Multi-Tree Genetic Programming with Elite Recombination (MTGPER) is proposed, which exploits the recombination of the excellent rules to obtain the better scheduling heuristics. The experimental results show that the proposed MTGPER significantly outperforms existing state-of-the-art methods. The scheduling heuristic evolved by MTGPER has quite good interpretability, which facilitates scheduling management in engineering practice.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107700"},"PeriodicalIF":6.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968059","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":"HephaestusForge: Optimal microservice deployment across the Compute Continuum via Reinforcement Learning","authors":"José Santos ,&nbsp;Mattia Zaccarini ,&nbsp;Filippo Poltronieri ,&nbsp;Mauro Tortonesi ,&nbsp;Cesare Stefanelli ,&nbsp;Nicola Di Cicco ,&nbsp;Filip De Turck","doi":"10.1016/j.future.2024.107680","DOIUrl":"10.1016/j.future.2024.107680","url":null,"abstract":"<div><div>With the advent of containerization technologies, microservices have revolutionized application deployment by converting old monolithic software into a group of loosely coupled containers, aiming to offer greater flexibility and improve operational efficiency. This transition made applications more complex, consisting of tens to hundreds of microservices. Designing effective orchestration mechanisms remains a crucial challenge, especially for emerging distributed cloud paradigms such as the Compute Continuum (CC). Orchestration across multiple clusters is still not extensively explored in the literature since most works consider single-cluster scenarios. In the CC scenario, the orchestrator must decide the optimal locations for each microservice, deciding whether instances are deployed altogether or placed across different clusters, significantly increasing orchestration complexity. This paper addresses orchestration in a containerized CC environment by studying a Reinforcement Learning (RL) approach for efficient microservice deployment in Kubernetes (K8s) clusters, a widely adopted container orchestration platform. This work demonstrates the effectiveness of RL in achieving near-optimal deployment schemes under dynamic conditions, where network latency and resource capacity fluctuate. We extensively evaluate a multi-objective reward function that aims to minimize overall latency, reduce deployment costs, and promote fair distribution of microservice instances, and we compare it against typical heuristic-based approaches. The results from an implemented OpenAI Gym framework, named as <em>HephaestusForge</em>, show that RL algorithms achieve minimal rejection rates (as low as 0.002%, 90x less than the baseline Karmada scheduler). Cost-aware strategies result in lower deployment costs (2.5 units), and latency-aware functions achieve lower latency (268–290 ms), improving by 1.5x and 1.3x, respectively, over the best-performing baselines. <em>HephaestusForge</em> is available in a public open-source repository, allowing researchers to validate their own placement algorithms. This study also highlights the adaptability of the DeepSets (DS) neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. The DS neural network can handle inputs and outputs as arbitrarily sized sets, enabling the RL algorithm to learn a policy not bound to a fixed number of clusters.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107680"},"PeriodicalIF":6.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968060","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":"A decentralized asynchronous federated learning framework for edge devices","authors":"Bin Wang ,&nbsp;Zhao Tian ,&nbsp;Jie Ma ,&nbsp;Wenju Zhang ,&nbsp;Wei She ,&nbsp;Wei Liu","doi":"10.1016/j.future.2024.107683","DOIUrl":"10.1016/j.future.2024.107683","url":null,"abstract":"<div><div>The traditional synchronous federated learning framework ensures global model consistency and accuracy. However, it is limited by the computational power differences between devices and the influence of non-IID data, which leads to inefficient training and insufficient model generalization performance. In this paper, we propose a decentralized asynchronous federated learning framework. The framework uses smart contracts deployed on the blockchain to manage edge devices for enhanced flexibility. At first, the framework performs model aggregation and validation through the use of consensus groups. It eliminates the potential single point of failure associated with centralized parameter servers. In addition, we propose a Federated Learning with Dynamically Growing Cache (FedDgc) method in a non-IID environment. The method reduces redundant gradient information exchange during initial feature extraction while maintaining the learning capability of the global model. Finally, the experimental results show that our framework has better test performance and guarantees the convergence speed of the model during training.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107683"},"PeriodicalIF":6.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166081","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":"Integrating scientific single-page applications with DevSecOps","authors":"Lance Drane,&nbsp;Marshall McDonnell,&nbsp;Randall Petras,&nbsp;Cody Stiner,&nbsp;Arthur J. Ruckman,&nbsp;Gavin M. Wiggins,&nbsp;Gregory Cage,&nbsp;Robert Smith,&nbsp;Seth Hitefield,&nbsp;Jesse McGaha,&nbsp;Andrew Ayres,&nbsp;Mike Brim,&nbsp;Richard Archibald,&nbsp;Addi Malviya-Thakur","doi":"10.1016/j.future.2024.107695","DOIUrl":"10.1016/j.future.2024.107695","url":null,"abstract":"<div><div>In the rapidly evolving field of frontend development, Single-Page Applications (SPAs) stand out for their ability to create dynamic and interactive web applications, particularly valuable in scientific software for their real-time data integration and complex workflow management. However, the process of creating a single-page web application development environment that accurately reflects the production environment isn’t always straightforward. Most SPA build systems assume configuration at build time, while DevSecOps engineers prefer runtime configuration. This paper proposes a unique, framework-agnostic methodology designed to bridge this divide, facilitating the seamless integration of SPAs within the DevSecOps framework without necessitating expertise in both domains. Leveraging environmental variables, Docker, and a strategic approach to Content Security Policy (CSP), we provide a comprehensive guide for developing, deploying, and securing SPAs in a manner that is both efficient and secure. Applying this method to the INTERSECT and Smart Spectral Matching platforms, we demonstrate its effectiveness in enhancing both the development process and the user experience in scientific applications, thereby addressing the complex challenges faced by research software engineers in the current landscape.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107695"},"PeriodicalIF":6.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166796","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":"Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain Internet of Things based framework","authors":"Osama Almurshed ,&nbsp;Ashish Kaushal ,&nbsp;Souham Meshoul ,&nbsp;Asmail Muftah ,&nbsp;Osama Almoghamis ,&nbsp;Ioan Petri ,&nbsp;Nitin Auluck ,&nbsp;Omer Rana","doi":"10.1016/j.future.2024.107696","DOIUrl":"10.1016/j.future.2024.107696","url":null,"abstract":"<div><div>The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107696"},"PeriodicalIF":6.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167490","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":"Parallel software design of large-scale diamond-structured crystals molecular dynamics simulation","authors":"Jianguo Liang ,&nbsp;Qianqian Li ,&nbsp;Hao Han ,&nbsp;You Fu","doi":"10.1016/j.future.2024.107694","DOIUrl":"10.1016/j.future.2024.107694","url":null,"abstract":"<div><div>Molecular dynamics (MD) simulation, a crucial technique for investigating atomic structure and dynamic properties, has become a primary method for studying the thermodynamic properties of dielectric materials, such as silicon, and their low-dimensional nanostructures. Diamond-structured semiconductors exhibit unique crystallographic properties. Achieving optimal simulation performance on supercomputing platforms necessitates specialized parallel design and optimization, considering both atom spatial characteristics and platform architecture. To tackle storage challenges in large-scale simulations of diamond-structured crystals, we designed a hierarchical storage-based atom data organization and a neighbor list construction algorithm exploiting positional offsets. Furthermore, a novel “point-line-plane” communication model was implemented. This model leverages the distribution of atom neighbors and a fixed neighbor list, enhancing communication efficiency via data packing to enable scalable simulations. A numerical simulation software, Diamond-MD, was developed for simulating diamond-structured crystals, enabling simulations at the 100 million-atom scale. Benchmark results indicate that Diamond-MD achieves a 44% reduction in memory usage and a 48% improvement in computational performance compared to LAMMPS. Moreover, Diamond-MD demonstrates excellent scalability.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107694"},"PeriodicalIF":6.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167345","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":"Cooperative metric learning-based hybrid transformer for automatic recognition of standard echocardiographic multi-views","authors":"Yiran Li ,&nbsp;Yankun Cao ,&nbsp;Jia Mi ,&nbsp;Xiaoxiao Cui ,&nbsp;Xifeng Hu ,&nbsp;Yuezhong Zhang ,&nbsp;Zhi Liu ,&nbsp;Lizhen Cui ,&nbsp;Shuo Li","doi":"10.1016/j.future.2024.107693","DOIUrl":"10.1016/j.future.2024.107693","url":null,"abstract":"<div><div>The successful recognition of the standard echocardiographic ten-views remains elusive, primarily due to the complexity of cardiac anatomy, confusion caused by low-quality data, and subtle variations among closely related multi-views. To cope with the limitations of existing algorithms, which include a lack of objectivity, accuracy, and robustness, we propose a Hybrid Cooperative Metric Network (HCMN). We enhance the objectivity, accuracy and robustness of the quality assessment by integrating knowledge of cycle-consistency with metric consistency, which helps mitigate inaccurate fitting in hybrid distributions. Therefore, it provides a clear feature similarity distribution to prevent feature confusion. The experiments demonstrate that the HCMN model significantly outperforms the state-of-the-art in quality assessment, achieving an impressive accuracy of 96.74%. We believe this novel framework will establish a reliable benchmark for recognizing standard echocardiographic multi-views and provide a new interpretable perspective on standardized the automatic cardiac disease diagnosis. By adapting and applying advanced assessment methodologies, we can enhance the clarity and interpretability of medical imaging, thereby aiding in the precise identification of lesions and improving decision-making accuracy in drug discovery.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107693"},"PeriodicalIF":6.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929251","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}