Future Generation Computer Systems-The International Journal of Escience最新文献_第7页

SHE-SFL: An efficient and privacy-preserving heterogeneous federated split learning architecture based on homomorphic encryption SHE-SFL：一种基于同态加密的高效且保护隐私的异构联邦分裂学习体系结构

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI: 10.1016/j.future.2025.108101

Jiaqi Xia , Meng Wu , Pengyong Li

{"title":"SHE-SFL: An efficient and privacy-preserving heterogeneous federated split learning architecture based on homomorphic encryption","authors":"Jiaqi Xia , Meng Wu , Pengyong Li","doi":"10.1016/j.future.2025.108101","DOIUrl":"10.1016/j.future.2025.108101","url":null,"abstract":"<div><div>Federated Learning (FL) and Split Learning (SL) are distributed methods that enable collaborative model training without sharing raw data. Combining FL and SL leverages the benefits of computational offloading and model privacy while enhancing efficiency through parallel processing. However, both methods risk privacy leaks: FL is vulnerable to model inversion attacks and gradient leaks, whereas SL’s data transmission during training can reveal sensitive information, potentially allowing attackers to reconstruct the original dataset. Current privacy protections often fall short of fully securing these systems and impose substantial computational and communication costs. In this work, we introduce SHE-SFL, an efficient privacy-preserving federated split learning architecture based on fully homomorphic encryption. Specifically, we employ the CKKS scheme to encrypt activation values during forward propagation, gradients during backpropagation, and the model parameters shared at the aggregation stage. This ensures that all data leaving the client domain is encrypted. This architecture includes two key modules: SHE-SL encrypts and transmits ciphertext based on batch packing and adopts a sparsification strategy, reducing system overhead and enabling polymorphic training of the models. SHE-Aggr enhances the efficiency of encrypting model parameters during the aggregation phase and perfectly supports encrypted weighted aggregation. Extensive experimental results demonstrate that the proposed SHE-SFL provides comprehensive protection for the federated split learning architecture with minimal impact on model performance, effectively safeguarding client privacy while significantly reducing system overhead.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108101"},"PeriodicalIF":6.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923010","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

HRL-D3: High resolution and lightweight defective data detection for IoT data integrity HRL-D3：用于物联网数据完整性的高分辨率和轻量级缺陷数据检测

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI: 10.1016/j.future.2025.108089

Suparna Kar, Kaif Ali Khan P, Ravi Surendra Nalawade, Vanga Aravind Shounik, Vikas Ravi Patil, Kotaro Kataoka

{"title":"HRL-D3: High resolution and lightweight defective data detection for IoT data integrity","authors":"Suparna Kar, Kaif Ali Khan P, Ravi Surendra Nalawade, Vanga Aravind Shounik, Vikas Ravi Patil, Kotaro Kataoka","doi":"10.1016/j.future.2025.108089","DOIUrl":"10.1016/j.future.2025.108089","url":null,"abstract":"<div><div>Due to the limited storage capacity of Internet of Things (IoT) devices, the use of third-party cloud storage service is an integral part of IoT based systems. Ensuring data integrity in cloud storage services is paramount for maintaining the safety and trustworthiness of the data generated and consumed by IoT applications. While verifying data integrity through defective data detection, the number of False Positives and False Negatives should be fewer so that the resolution is higher. However, increasing the resolution also incurs an increase in metadata for integrity verification and results in higher storage overhead. This paper proposes High Resolution and Lightweight Defective Data Detection (HRL-D3) for IoT data integrity with a short verification time, low storage overhead and minimal computational cost. HRL-D3 introduces 1) the use of Merkle Hash Tree and the novel concept of Intermediate Hash for enabling faster Data Integrity Verification (DIV) and higher resolution, and 2) an Adaptive Data Chunking Algorithm for balancing the trade-off between resolution and storage overhead. Our security analysis examined the risks of potential attacks to HRL-D3, and outlined the prevention provided by the proposed solution as well as the mitigation through an operational workaround. A Proof of Concept implementation HRL-D3 was evaluated and demonstrated its effectiveness in balancing the trade off between the resolution and the storage overhead tradeoff as well as achieving low-latency DIV.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108089"},"PeriodicalIF":6.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925379","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 genetic algorithm with selective repair method under combined-criteria for deadline-constrained IoT workflow scheduling in Fog–Cloud computing 雾云计算下限期物联网工作流调度的组合准则选择修复遗传算法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI: 10.1016/j.future.2025.108050

Amer Saeed , Gang Chen , Hui Ma , Qiang Fu

{"title":"A genetic algorithm with selective repair method under combined-criteria for deadline-constrained IoT workflow scheduling in Fog–Cloud computing","authors":"Amer Saeed , Gang Chen , Hui Ma , Qiang Fu","doi":"10.1016/j.future.2025.108050","DOIUrl":"10.1016/j.future.2025.108050","url":null,"abstract":"<div><div>Many IoT systems require deadline-constrained workflow scheduling, where missed deadlines can have serious consequences. Scheduling such IoT workflows in Fog–Cloud environments is challenging due to resource heterogeneity and the variability in workflow patterns and deadlines. Existing approaches, including heuristic and meta-heuristic algorithms, often fail to reliably satisfy deadline constraints while simultaneously minimizing the cost associated with the computational resources used for executing workflows. This paper introduces the Internet of Things Genetic Algorithm with Selective Repair under Combined Criteria (IoTGA-SRC<sup>2</sup>) to effectively tackle these challenges. IoTGA-SRC<sup>2</sup> introduces a novel selection mechanism that prioritizes solutions based on deadline violations and execution costs. It also features an innovative repair method, which can systematically detect infeasible solutions, perform a root cause analysis to identify the key factors causing deadline violations, and reallocate critical tasks using a multi-criteria method. By properly managing delays caused by execution time, communication time, and waiting time, IoTGA-SRC<sup>2</sup> can consistently satisfy deadline constraints across a wide range of problem configurations. Extensive experiments demonstrate that IoTGA-SRC<sup>2</sup> consistently outperforms multiple state-of-the-art methods in reducing execution costs while adhering to stringent deadline constraints, making it a valuable choice for various real-world applications in heterogeneous IoT–Fog–Cloud computing environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108050"},"PeriodicalIF":6.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923008","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 lightweight blockchain-based defense method for federated self-supervised learning 一种轻量级的基于区块链的联邦自监督学习防御方法

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI: 10.1016/j.future.2025.108092

Hadiseh Rezaei , Marjan Golmaryami , Hadis Rezaei , Francesco Palmieri

{"title":"A lightweight blockchain-based defense method for federated self-supervised learning","authors":"Hadiseh Rezaei , Marjan Golmaryami , Hadis Rezaei , Francesco Palmieri","doi":"10.1016/j.future.2025.108092","DOIUrl":"10.1016/j.future.2025.108092","url":null,"abstract":"<div><div>In recent years, deep learning technologies have experienced significant advancements, especially in the computer vision area. However, their success depends heavily on the availability of vast amounts of labeled data, introducing several data-gathering issues. To cope with these limitations, we use Federated Self-Supervised Learning (FedSSL), a framework that integrates Self-Supervised Learning (SSL) with Federated Learning (FL). FedSSL uses unlabeled data to enhance model performance and generalization without compromising data privacy. Despite its advantages, our research reveals vulnerabilities in FedSSL, such as susceptibility to model poisoning attacks. We introduce a Blockchain-based defense method for FedSSL (BCH-FedSSL) to face this risk, which incorporates blockchain technology to decentralize model aggregation, ensuring data integrity and transparency. Experimental results conducted under IID data distributions using the CIFAR-10, CIFAR-100, and Fashion-MNIST datasets demonstrate that BCH-FedSSL’s effectiveness in maintaining model accuracy and robustness under adversarial conditions. The proposed method achieved, in the presence of a poisoning attack, a 30 % performance improvement on CIFAR-10, a 27 % on CIFAR-100, and a 31 % on Fashion-MNIST. This study highlights the potential of combining blockchain with federated learning to create secure, scalable, and efficient decentralized learning systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108092"},"PeriodicalIF":6.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988389","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

CLO: Controller load optimization using multi-step load prediction for software-defined internet of things CLO：基于多步负载预测的软件定义物联网控制器负载优化

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI: 10.1016/j.future.2025.108104

Yuanhang Ge , Yong Liu , Qian Meng , Zihang Chen

{"title":"CLO: Controller load optimization using multi-step load prediction for software-defined internet of things","authors":"Yuanhang Ge , Yong Liu , Qian Meng , Zihang Chen","doi":"10.1016/j.future.2025.108104","DOIUrl":"10.1016/j.future.2025.108104","url":null,"abstract":"<div><div>Software-Defined Internet of Things (SD-IoT) is a novel network architecture that integrates Software-Defined Networking (SDN) with Internet of Things (IoT) technologies. As the network scales up, increasing service requests impose a heavier processing burden on the control plane, resulting in load imbalance among controllers. Existing switch migration mechanisms have been proposed to optimize controller load. Unfortunately, most current approaches rely solely on real-time network information or the network state in the next time period, which fails to identify overloaded controllers effectively. Moreover, they overlook the load variation trends of switches in the process of switch selection, leading to suboptimal results. More critically, existing methods often struggle to balance load balancing effectiveness and migration cost when selecting target controllers. To address these issues, we propose controller load optimization using multi-step load prediction (CLO) scheme. This scheme adopts the decomposition-based linear model (DLinear) for multi-step load prediction, which helps avoid unnecessary migrations. We further incorporate the Weighted Least Squares (WLS) method to analyze the load trend of each switch, enabling intelligent identification of candidate switches for migration. In addition, we propose a target controller selection algorithm based on an improved Zebra Optimization Algorithm (ZOA), which significantly reduces load imbalance and migration cost. Our approach is based on two assumptions. Firstly, all controllers cannot be overloaded simultaneously. Secondly, each switch can only be connected to one master controller. Under these assumptions, we conduct experiments using Mininet as the emulation platform and Ryu as the controller. Experimental results show that, compared with existing approaches, CLO scheme reduces the average load imbalance rate by 21.3 % and the average response time by 14.1 %.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108104"},"PeriodicalIF":6.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925288","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

Wind power forecasting using multivariate signal decomposition and stacked GRU ensembles with error correction 基于多元信号分解和误差校正的叠加GRU集成的风电预测

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-25 DOI: 10.1016/j.future.2025.108105

Poonam Dhaka, Mini Sreejeth, M.M. Tripathi

{"title":"Wind power forecasting using multivariate signal decomposition and stacked GRU ensembles with error correction","authors":"Poonam Dhaka, Mini Sreejeth, M.M. Tripathi","doi":"10.1016/j.future.2025.108105","DOIUrl":"10.1016/j.future.2025.108105","url":null,"abstract":"<div><div>Accurate wind power forecasting is vital for enhancing power system reliability, security, and cost efficiency. Recent advancements have seen the rise of ensemble systems for short-term wind power prediction. However, traditional ensembles often use conventional pre-processing and fixed-weight sub-model integration, limiting their effectiveness. This study introduces a novel hybrid ensemble system for wind farm generation forecasting that integrates multivariate signal decomposition, deep learning, and prediction error correction. The proposed system utilizes an innovative data preprocessing technique that addresses wind series non-stationarity by decomposing the series into intrinsic mode functions and a residual component. Stacked Gated Recurrent Unit (GRU) networks are then utilized to make separate predictions for each decomposed series, with the GRU structures adjusted based on the decomposition levels to create diverse forecasters. The final predictions are refined with a Bagging-Boosting mechanism, improving accuracy and capturing trends effectively. Testing on real-world data from the Tuticorin wind farm in Tamil Nadu, India, included five comprehensive experiments to assess stability and forecasting ability. Results demonstrated the proposed system’s superior performance over single models and other hybrid ensembles, providing more precise and reliable wind power forecasts.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108105"},"PeriodicalIF":6.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996390","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

Towards smarter live migration: Minimizing SLO violations and costs 走向更智能的实时迁移：最小化SLO违规和成本

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-24 DOI: 10.1016/j.future.2025.108085

Youngsu Cho , Changyeon Jo , Reza Entezari-Maleki , Jörn Altmann , Bernhard Egger

{"title":"Towards smarter live migration: Minimizing SLO violations and costs","authors":"Youngsu Cho , Changyeon Jo , Reza Entezari-Maleki , Jörn Altmann , Bernhard Egger","doi":"10.1016/j.future.2025.108085","DOIUrl":"10.1016/j.future.2025.108085","url":null,"abstract":"<div><div>Data centers employ live virtual machine (VM) migration to optimize resource usage while ensuring continuous execution of guest operating systems. Given the current resource utilization, sophisticated algorithms determine when and where to migrate which VMs. Surprisingly little attention, however, is given to selecting the appropriate migration technique based on specific host and guest workload characteristics. This work first shows that relying on a single live migration algorithm leads to significantly more Service-Level Objective (SLO) violations and higher resource usage than adaptively selecting the most suitable migration algorithm. Building on this observation, we then present an intelligent live migration framework that selects the most appropriate live migration algorithm based on SLOs and operational cost factors, using a multi-objective optimization approach. Through a comprehensive evaluation across diverse hotspot and consolidation scenarios, we demonstrate that the presented framework is able to substantially reduce SLO violation while optimizing key operational metrics. The framework reduces the total migration time by a factor of 1.5 and decreases SLO violations by nearly an order of magnitude compared to the predominantly used pre-copy method. Moreover, it achieves near-optimal VM migration technique selection compared to an Oracle under varying workload conditions. The results indicate that intelligent selection of live migration algorithms can significantly enhance both application performance and resource efficiency in virtualized environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108085"},"PeriodicalIF":6.2,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923997","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

Collaborative anomaly detection in log data: Comparative analysis and evaluation framework 日志数据协同异常检测：比较分析与评价框架

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-24 DOI: 10.1016/j.future.2025.108090

André García Gómez , Max Landauer , Markus Wurzenberger , Florian Skopik , Edgar Weippl

{"title":"Collaborative anomaly detection in log data: Comparative analysis and evaluation framework","authors":"André García Gómez , Max Landauer , Markus Wurzenberger , Florian Skopik , Edgar Weippl","doi":"10.1016/j.future.2025.108090","DOIUrl":"10.1016/j.future.2025.108090","url":null,"abstract":"<div><div>Log Anomaly Collaborative Intrusion Detection Systems (CIDS) are designed to detect suspicious activities and security breaches by analyzing log files using anomaly detection techniques while leveraging collaboration between multiple entities (e.g., different systems, organizations, or network nodes). Unlike traditional Intrusion Detection Systems (IDS) that require centralized algorithm updates and data aggregation, CIDS enable decentralized updates without extensive data exchange, improving efficacy, scalability, and compliance with regulatory constraints. Additionally, inter-detector communication helps to reduce the number of false positives. These systems are particularly useful in distributed environments, where individual system have limited visibility into potential threats. This paper reviews the current landscape of Log Anomaly CIDS and introduces an open-source framework designed to create benchmark datasets for evaluating system performance. We categorize log anomaly detectors into three categories: Sequential-wise, Embedding-wise, and Graph-wise. Furthermore, our open framework facilitates rigorous evaluation against different challenges identifying weaknesses in existing methods like Deeplog and enhancing model robustness.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108090"},"PeriodicalIF":6.2,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923957","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

Intrusion detection with improved quantum neural network: A bigdata perspective 基于改进量子神经网络的入侵检测：大数据视角

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-23 DOI: 10.1016/j.future.2025.108102

Nithya BN, Hemanth Uppala

{"title":"Intrusion detection with improved quantum neural network: A bigdata perspective","authors":"Nithya BN, Hemanth Uppala","doi":"10.1016/j.future.2025.108102","DOIUrl":"10.1016/j.future.2025.108102","url":null,"abstract":"<div><div>An Intrusion Detection System (IDS) is a pivotal component of cybersecurity infrastructure which is designed to protect networks, systems, and data from unauthorized access, misuse, or malicious activities. Its primary function is to monitor network or system activities in real-time that analyze incoming traffic and identify any anomalous behavior or patterns that deviate from established norms or signatures of known attacks. Both conventional ML and DL-based IDS may be subject to adversarial attacks, where malicious actors deliberately operate input data to evade detection. Consequently, a proposed solution involves the development of an ID model based on Improved Quantum Neural Network and LinkNet (IQNN-LinkNet) architecture aimed at addressing the aforementioned challenges. This paper adopts a methodical process encompassing pre-processing, handling the bigdata, and intrusion detection. The input data is first subjected to pre-processing via the Improved min-max normalization technique. Subsequently, the bigdata is handled via MRF which also incorporates feature extraction procedures. These extracted features are then utilized as input for a hybrid detection model that integrates IQNN and LinkNet classifiers. Extensive analyses are used to validate the effectiveness of the suggested IQNN-LinkNet model through simulation and experimental evaluations. Eventually, this paper presents a robust and confirmed model for intrusion detection which highlights the potential of the IQNN-LinkNet model particularly in bigdata applications.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108102"},"PeriodicalIF":6.2,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923009","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

Cost-efficient quantum cloud task offloading with quantum-inspired particle swarm optimization 成本效益量子云任务卸载与量子启发粒子群优化

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-22 DOI: 10.1016/j.future.2025.108095

Santanu Ghosh, Pratyay Kuila

{"title":"Cost-efficient quantum cloud task offloading with quantum-inspired particle swarm optimization","authors":"Santanu Ghosh, Pratyay Kuila","doi":"10.1016/j.future.2025.108095","DOIUrl":"10.1016/j.future.2025.108095","url":null,"abstract":"<div><div>Quantum cloud computing (QCC) empowers application users (AUs) to manage computationally intensive and resource-demanding applications, particularly those involving intractable and complex problems. This research focuses on quantum task offloading (QTO) within the QCC environment. Successful QTO decisions require careful consideration of energy consumption, execution delay, service cost, and load balancing. Incorporating task urgency, the quantum task offloading problem (QTOP) is mathematically formulated to prioritize the execution of urgent tasks while satisfying budget and deadline constraints. It is shown that QTOP is a non-deterministic polynomial-time (NP-complete) problem. To address this challenge, a quantum-inspired particle swarm optimization (QPSO) algorithm is proposed. A novel quantum particle (QP) encoding scheme is introduced and decoded using a linear hashing approach to generate valid task offloading solutions. An effective fitness function is designed by integrating two penalty variables to eliminate infeasible solutions that violate resource and budget constraints. Extensive simulations are conducted to evaluate the performance of QPSO against several baseline algorithms, where QPSO consistently outperforms the others. Furthermore, the proposed cost model is benchmarked against existing models, demonstrating superior efficiency. Statistical analysis, as well as exploration and exploitation behavior analysis, further validate the robustness of the proposed method.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108095"},"PeriodicalIF":6.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916539","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