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

{"title":"Towards sustainable smart cities: Workflow scheduling in cloud of health things (CoHT) using deep reinforcement learning and moth flame optimization for edge–cloud systems","authors":"Mustafa Ibrahim Khaleel","doi":"10.1016/j.future.2025.107821","DOIUrl":"10.1016/j.future.2025.107821","url":null,"abstract":"<div><div>In smart cities, the Cloud of Health Things (CoHT) enhances service delivery and optimizes task scheduling and allocation. As CoHT systems proliferate and offer a range of services with varying Quality of Service (QoS) demands, servers face the challenge of efficiently distributing limited virtual machines across internet-based applications. This can strain performance, particularly for latency-sensitive healthcare applications, resulting in increased delays. Edge computing mitigates this issue by bringing computational, storage, and network resources closer to the data source, working in tandem with cloud computing. Combining edge and cloud computing is essential for improving efficiency, especially for IoT-driven tasks where reliability and low latency are vital concerns. This paper introduces an intelligent task scheduling and allocation model that leverages the Moth Flame Optimization (MFO) algorithm, integrated with deep reinforcement learning (DRL), to optimize edge–cloud computing in sustainable smart cities. The model utilizes a bi-class neural network to classify tasks, ensuring rapid convergence while delivering both local and globally optimal solutions, achieving efficient resource allocation, and enhancing QoS. The model was trained on real-world and synthesized cluster datasets, including the Google cluster dataset, to learn cloud-based job scheduling, which is then applied in real-time. Compared with DRL and non-DRL approaches, the model shows significant performance gains, with a 76.2% reduction in latency, an 81.9% increase in reliability, a 74.4% improvement in resource utilization, and an 83.1% enhancement in QoS.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"170 ","pages":"Article 107821"},"PeriodicalIF":6.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739223","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":"MinCache: A hybrid cache system for efficient chatbots with hierarchical embedding matching and LLM","authors":"Keihan Haqiq ,&nbsp;Majid Vafaei Jahan ,&nbsp;Saeede Anbaee Farimani ,&nbsp;Seyed Mahmood Fattahi Masoom","doi":"10.1016/j.future.2025.107822","DOIUrl":"10.1016/j.future.2025.107822","url":null,"abstract":"<div><div>Large Language Models (LLMs) have emerged as powerful tools for various natural language processing tasks such as multi-agent chatbots, but their computational complexity and resource requirements pose significant challenges for real-time chatbot applications. Caching strategies can alleviate these challenges by reducing redundant computations and improving response times. In this paper, we propose MinCache, a novel hybrid caching system tailored for LLM applications. Our system employs a hierarchical cache strategy for string retrieval, performing exact match lookups first, followed by resemblance matching, and finally resorting to semantic matching to deliver the most relevant information. MinCache combines the strengths of Least Recently Used (LRU) cache and string fingerprints caching techniques, leveraging MinHash algorithm for fast the <em>resemblance</em> matching. Additionally, Mincache leverage a sentence-transformer for estimating <em>semantics</em> of input prompts. By integrating these approaches, MinCache delivers high cache hit rates, faster response delivery, and improved scalability for LLM applications across diverse domains. Our experiments demonstrate a significant acceleration of LLM applications by up to <span>4.5X</span> against GPTCache as well as improvements in accurate cache hit rate. We also discuss the scalability of our proposed approach across medical domain chat services.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"170 ","pages":"Article 107822"},"PeriodicalIF":6.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714393","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":"Dynamic class-balanced threshold Federated Semi-Supervised Learning by exploring diffusion model and all unlabeled data","authors":"Zeyuan Wang ,&nbsp;Yang Liu ,&nbsp;Guirong Liang ,&nbsp;Cheng Zhong ,&nbsp;Feng Yang","doi":"10.1016/j.future.2025.107820","DOIUrl":"10.1016/j.future.2025.107820","url":null,"abstract":"<div><div>Federated Semi-Supervised Learning (FSSL) aims to train models based on federated learning using a small amount of labeled data and a large amount of unlabeled data. The limited labeled data and the issue of non-independent and identically distributed (non-IID) data are the major challenges faced by FSSL. Most of the previous methods use traditional fixed thresholds to filter out high-confidence samples and assign pseudo-labels to them without considering low-confidence samples. These methods then increase the sample space by random sampling and other techniques to address the challenges of FSSL. However, the performance of these models remains unsatisfactory. To tackle these challenges, we propose DDRFed, a novel FSSL framework that effectively utilizes all available data by integrating a diffusion model and dynamic class balance thresholds. Specifically, we first mitigate the client-side non-IID issue by utilizing a dataset generated by a client-side co-trained diffusion model that conforms to the global data distribution. The local clients then use the global class distribution information provided by the server to establish dynamic class balance thresholds, which distinguish between high-confidence and low-confidence samples. The existence of dynamic thresholds ensures a sufficient amount of labeled data during the training process. Meanwhile, to fully leverage the knowledge contained in low-confidence samples, we optimize the model’s performance through residual class negative learning. Experiments conducted on two natural datasets demonstrate the superiority of DDRFed, addressing both major challenges in FSSL.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"170 ","pages":"Article 107820"},"PeriodicalIF":6.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704950","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":"A distributed identity management and cross-domain authentication scheme for the Internet of Things","authors":"Miaomiao Wang,&nbsp;Ze Wang","doi":"10.1016/j.future.2025.107818","DOIUrl":"10.1016/j.future.2025.107818","url":null,"abstract":"<div><div>Reliable identity management and authentication are prerequisites for secure information communication. Traditional centralized schemes rely on the Certificate Authority (CA), and their cross-domain authentication is complex, posing a risk of centralized data leakage. The advancement of blockchain technology has disrupted the traditional model, leading to the emergence of Self-Sovereign Identity (SSI) management and authentication schemes. However, the widespread adoption of SSI still faces some challenges, such as key loss and the inefficiency of MerkleTree verification. Therefore, we propose an improved distributed identity management and cross-domain authentication scheme for the Internet of Things (IoT). In this scheme, a key creation and recovery mechanism is first proposed to prevent identity unavailability caused by key loss. Then, a double one-way accumulator algorithm is designed to improve identity authentication and enhance the authentication efficiency. Our scheme has passed formal and informal security analyses, and has robust performance.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107818"},"PeriodicalIF":6.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681465","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":"RAANMF: An adaptive sequence feature representation method for predictions of protein thermostability, PPI, and drug–target interaction","authors":"Qunfang Yan,&nbsp;Shuyi Pan,&nbsp;Zhixing Cheng,&nbsp;Yanrui Ding","doi":"10.1016/j.future.2025.107819","DOIUrl":"10.1016/j.future.2025.107819","url":null,"abstract":"<div><div>The effective representation of sequence is essential for analyzing protein structure and function. Sequence representation based on reduced amino acids plays an important part in protein research, as it preserves key sequence features while simplifying feature processing. However, it is a challenge to select an appropriate reduced amino acid method for various downstream analysis tasks. Developing reduced amino acid methods that can adapt to various downstream tasks is essential to promote protein-related researches. In this paper, we propose a novel reduced amino acid method based on non-negative matrix factorization (NMF) named RAANMF, which can adaptively generate the reduced amino acid schemes for different tasks. Through validating the effectiveness and universality of RAANMF on three mainstream tasks including protein thermostability prediction, protein–protein interaction prediction, and drug–target interaction prediction, the results demonstrate that the reconstructed models using RAANMF to characterize amino acid sequences can achieve comparable or superior predictive performance with greatly reduced feature dimensions compared to the original models. Moreover, the interpretability of RAANMF which is analyzed from the perspective of the non-negative matrix clustering principle helps us understand the biological significance and enhances its credibility and utility in practical applications. As a method developed from NMF, RAANMF offers a straightforward and interpretable approach for extracting latent features, and it is expected to help study the relation of protein sequence, structure and function.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107819"},"PeriodicalIF":6.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681470","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":"Prediction model of performance–energy trade-off for CFD codes on AMD-based cluster","authors":"Marcin Lawenda ,&nbsp;Łukasz Szustak ,&nbsp;László Környei","doi":"10.1016/j.future.2025.107810","DOIUrl":"10.1016/j.future.2025.107810","url":null,"abstract":"<div><div>This work explores the importance of performance–energy correlation for CFD codes, highlighting the need for sustainable and efficient use of clusters. The prime goal includes the optimisation of selecting and predicting the optimal number of computational nodes to reduce energy consumption and/or improve calculation time. In this work, the utilisation cost of the cluster, measured in core-hours, is used as a crucial factor in energy consumption and selecting the optimal number of computational nodes. The work is conducted on the cluster with AMD EPYC Milan-based CPUs and OpenFOAM application using the Urban Air Pollution model. In order to investigate performance–energy correlation on the cluster, the <span>CVOPTS</span> (Core VOlume Points per TimeStep) metric is introduced, which allows a direct comparison of the parallel efficiency for applications in modern HPC architectures. This metric becomes essential for evaluating and balancing performance with energy consumption to achieve cost-effective hardware configuration. The results were confirmed by numerous tests on a 40-node cluster, considering representative grid sizes. Based on the empirical results, a prediction model was derived that takes into account both the computational and communication costs of the simulation. The research reveals the impact of the AMD EPYC architecture on superspeedup, where performance increases superlinearly with the addition of more computational resources. This phenomenon enables a priori the prediction of performance–energy trade-offs (computing-faster or energy-save setups) for a specific application scenario, through the utilisation of varying quantities of computing nodes.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107810"},"PeriodicalIF":6.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681466","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":"HS-GIoV: High-speed green internet of vehicles (IoV) edge-assisted model for low-latency inference in autonomous driving","authors":"Oshin Rawlley,&nbsp;Shashank Gupta,&nbsp;Kashish Mahajan,&nbsp;Aishna Shrivastava,&nbsp;Esha Jain","doi":"10.1016/j.future.2025.107817","DOIUrl":"10.1016/j.future.2025.107817","url":null,"abstract":"<div><div>Green IoV has emerged as a latent solution in the field of autonomous driving for the future Intelligent transportation system (ITS) accompanied with green wireless communication and computational intelligence. It facilitates enhanced traffic management applications, reduced traffic congestion and compatible V2X connectivity. However, GIoV faces significant challenges in providing seamless bandwidth for real-time video analytics, especially under adverse environments, with improved accuracy in autonomous driving. Although deep neural networks (DNN) are effective in locating vehicles, they struggle to frequently access the edge network and maintain accuracy. In addition, their substantial computational demands waste energy and render them infeasible to deploy on resource-constrained devices for low-latency real-time inference. In this paper, we propose a high-speed GIoV (HS-GIoV) framework that models the problem of energy-efficient video analytics accuracy over multiple time-periods using Lyapunov optimization. To solve this problem, we have proposed a novel on-the-fly Traffic Stream Object Detection (TSOD) algorithm which is lightweight and triggers the re-training only when there is an accuracy decline, thereby avoiding unnecessary computations. We have also proposed a heuristic algorithm that solves seamless bandwidth issue using Lagrangian relaxation. We have tested the HS-GIoV on the self-driving kit that comprises NVIDIA high-end devices. It enhances the accuracy around 20% and reduces the training time to approx. 55%.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107817"},"PeriodicalIF":6.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681464","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":"Causal invariant geographic network representations with feature and structural distribution shifts","authors":"Yuhan Wang ,&nbsp;Silu He ,&nbsp;Qinyao Luo ,&nbsp;Hongyuan Yuan ,&nbsp;Ling Zhao ,&nbsp;Jiawei Zhu ,&nbsp;Haifeng Li","doi":"10.1016/j.future.2025.107814","DOIUrl":"10.1016/j.future.2025.107814","url":null,"abstract":"<div><div>Relationships between geographic entities, including human-land and human-people relationships, can be naturally modelled by graph structures, and geographic network representation is an important theoretical issue. The existing methods learn geographic network representations through deep graph neural networks (GNNs) based on the i.i.d. assumption. However, the spatial heterogeneity and temporal dynamics of geographic data make the out-of-distribution (OOD) generalisation problem particularly salient. We classify geographic network representations into invariant representations that always stabilise the predicted labels under distribution shifts and background representations that vary with different distributions. The latter are particularly sensitive to distribution shifts (feature and structural shifts) between testing and training data and are the main causes of the out-of-distribution generalisation (OOD) problem. Spurious correlations are present between invariant and background representations due to selection biases/environmental effects, resulting in the model extremes being more likely to learn background representations. The existing approaches focus on background representation changes that are determined by shifts in the feature distributions of nodes in the training and test data while ignoring changes in the proportional distributions of heterogeneous and homogeneous neighbour nodes, which we refer to as structural distribution shifts. We propose a feature-structure mixed invariant representation learning (FSM-IRL) model that accounts for both feature distribution shifts and structural distribution shifts. To address structural distribution shifts, we introduce a sampling method based on causal attention, encouraging the model to identify nodes possessing strong causal relationships with labels or nodes that are more similar to the target node. This approach significantly enhances the invariance of the representations between the source and target domains while reducing the dependence on background representations that arise by chance or in specific patterns. Inspired by the Hilbert–Schmidt independence criterion, we implement a reweighting strategy to maximise the orthogonality of the node representations, thereby mitigating the spurious correlations among the node representations and suppressing the learning of background representations. In addition, we construct an educational-level geographic network dataset under out-of-distribution (OOD) conditions. Our experiments demonstrate that FSM-IRL exhibits strong learning capabilities on both geographic and social network datasets in OOD scenarios.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107814"},"PeriodicalIF":6.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677904","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":"Chained continuous quantum federated learning framework","authors":"Dev Gurung,&nbsp;Shiva Raj Pokhrel","doi":"10.1016/j.future.2025.107800","DOIUrl":"10.1016/j.future.2025.107800","url":null,"abstract":"<div><div>The integration of quantum machine learning into federated learning paradigms is poised to transform the future of technologies that depend on diverse machine learning methodologies. This research delves into Quantum Federated Learning (QFL), presenting an initial framework modeled on the Federated Averaging (FedAvg) algorithm, implemented via Qiskit. Despite its potential, QFL encounters critical challenges, including (i) susceptibility to a single point of failure, (ii) communication bottlenecks, and (iii) uncertainty in model convergence. Subsequently, we dive deeper into QFL and propose an innovative alternative to traditional server-based QFL. Our approach introduces a chained continuous QFL framework (ccQFL), which eliminates the need for a central server and the FedAvg method. In our framework, clients engage in a chained continuous training process, where they exchange models and collaboratively enhance each other’s performance. This approach improves both the efficiency of communication and the accuracy of the training process. Our experimental evaluation includes a proof-of-concept to demonstrate initial feasibility and a prototype study simulating TCP/IP communication between clients. This simulation enables concurrent operations, verifying the potential of ccQFL for real-world applications. We examine various datasets, including Iris, MNIST, synthetic and Genomic, covering a range of data sizes from small to large. For further validity of our proposed method, we extend our experimental analysis in other frameworks such as PennyLane and TensorCircuit where we include various ablation studies covering major considerations and factors that impact the framework to study validity, robustness, practicality, and others. Our results show that the ccQFL framework achieves model convergence, and we evaluate other critical metrics such as performance and communication delay. In addition, we provide a theoretical analysis to establish and discuss many factors such as model convergence, communication costs, etc.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107800"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677751","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":"An accurate and efficient self-distillation method with channel-based feature enhancement via feature calibration and attention fusion for Internet of Things","authors":"Qian Zheng ,&nbsp;Shengbo Chen ,&nbsp;Guanghui Wang ,&nbsp;Linfeng Li ,&nbsp;Shuo Peng ,&nbsp;Zhonghao Yao","doi":"10.1016/j.future.2025.107816","DOIUrl":"10.1016/j.future.2025.107816","url":null,"abstract":"<div><div>With the rise of the Internet of Things (IoT), using convolutional neural networks (CNNs) for image tasks on edge devices has become prevalent, but the increased size and complexity of neural networks for better performance is not ideal for resource-limited embedded devices. Self-distillation, which does not need a pre-trained complex model, has been introduced to utilize knowledge distillation during the model’s own training, thus enhancing performance. However, the model accuracy and efficiency of current self-distillation techniques still need investigation to meet real-world demands in IoT scenarios. Therefore, this paper proposes an improved self-distillation with Channel-Based Feature Enhancement (CBFE) via feature calibration and attention fusion, which improves network performance with minimal extra load. In particular, we first propose a channel-based feature calibration module. This module uses 1x1 convolutions to reduce and then restore the channel dimension of the neural network output feature maps. For each input feature map, it generates a new feature map, which is then element-wise multiplied with the original feature map to enhance representation. Second, we introduce a channel attention-based feature fusion network branch that refines a more accurate feature representation to better guide the training of shallow layers of the network. Experimental results show that our method surpasses the state-of-the-art methods, demonstrating enhanced performance and generalization on various benchmarks.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"169 ","pages":"Article 107816"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677752","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}