Concurrency and Computation-Practice & Experience最新文献_第9页

Discovering and Ranking Urban Social Clusters Out of Streaming Social Media Datasets 从流式社交媒体数据集中发现城市社交集群并对其进行排名

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-24 DOI: 10.1002/cpe.8314

Mete Celik, Ahmet Sakir Dokuz, Alper Ecemis, Emre Erdogmus

{"title":"Discovering and Ranking Urban Social Clusters Out of Streaming Social Media Datasets","authors":"Mete Celik, Ahmet Sakir Dokuz, Alper Ecemis, Emre Erdogmus","doi":"10.1002/cpe.8314","DOIUrl":"https://doi.org/10.1002/cpe.8314","url":null,"abstract":"<div>\u0000 \u0000 <p>Urban social media mining is the process of discovering urban patterns from spatio-temporal social media datasets. Urban social clusters are the clusters formed by the social media posts of users living in cities at a certain time and place. Discovering and identifying urban social clusters is of great importance for urban and regional planning, target audience identification, a better understanding of city dynamics and so forth. Discovering and ranking urban social clusters out of streaming social media datasets require efficient filtering approaches and mining algorithms. In the literature, there are several studies performed that address the discovery of the importance of urban clusters. Most of these studies take into account the spatial expansions over time and the changes in the numbers of elements within clusters when identifying the significance of urban clusters. However, in contrast to these studies, we have also considered cluster temporal formation stability, spatial density variation, and the impact of meta-information on urban social clusters. In this study, Temporal, Spatial, and Meta Important Urban Social Clusters Miner (TSMIUSC-Miner) algorithm is proposed. In the proposed algorithm, urban social clusters are discovered, and their importance relative to each other are compared and ranked. The temporal, spatial and meta importance scores of the clusters are calculated and then, the clusters that satisfy predefined score thresholds are discovered. The performance of the proposed TSMIUSC-Miner algorithm compared with that of a naive approach using real-life streaming Twitter/X dataset. The results showed that the proposed TSMIUSC-Miner algorithm outperforms the naive approach in terms of execution time.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Request Deadline Split and Interference-Aware Request Migration in Edge Cloud 边缘云中请求截止日期分割和干扰感知请求迁移

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-24 DOI: 10.1002/cpe.8315

Jie Wang, Huiqun Yu, Guisheng Fan, Jiayin Zhang

{"title":"Request Deadline Split and Interference-Aware Request Migration in Edge Cloud","authors":"Jie Wang, Huiqun Yu, Guisheng Fan, Jiayin Zhang","doi":"10.1002/cpe.8315","DOIUrl":"https://doi.org/10.1002/cpe.8315","url":null,"abstract":"<div>\u0000 \u0000 <p>Edge computing extends computing resources from the data center to the edge of the network to better handle latency-sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency-sensitive tasks, a feasible solution is to offload some latency-sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub-problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi-resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one-third the number of request violations of other methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Multiobjective Approach for E-Commerce Website Structure Optimization 电子商务网站结构优化的多目标方法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-23 DOI: 10.1002/cpe.8302

Shina Panicker, T. V. Vijay Kumar, Divakar Yadav

{"title":"A Multiobjective Approach for E-Commerce Website Structure Optimization","authors":"Shina Panicker, T. V. Vijay Kumar, Divakar Yadav","doi":"10.1002/cpe.8302","DOIUrl":"https://doi.org/10.1002/cpe.8302","url":null,"abstract":"<div>\u0000 \u0000 <p>Complex websites comprise a variety of diverse web entities, which require constant restructuring resonating with the latest trends, shifting consumer expectations and market driven changes. Therefore, designing suitable models to optimally restructure such websites is of paramount importance and must take into consideration several factors about the web entities such as display size, download time, type, location in the page, sales likelihood, discounts, and the ongoing trend. A recent study has taken all these attributes into consideration and designed a model based on the Access Score, Interface Score, and Purchase Score. However, this model suffers from certain drawbacks such as it did not address the underlying cohesiveness between these attributes. Further, it provided a single optimal solution to the adaptive website structure optimization (<i>AWSO</i>) problem and relied on the a priori knowledge of weights. The basis of the new proposed model is that there can be more than one optimal solution to the <i>AWSO</i> problem in the real world. The novel tri-objective optimization model uses <i>NSGA-II</i> algorithm to simultaneously optimize the attributes and finds advantageous trade-off solutions without requiring a priori knowledge of weights. The proposed <i>MO-AWSO</i><sub><i>NSGA-II</i></sub> model is shown to outperform the existing model proving it better suited for the <i>AWSO</i> problem.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Workflow Scheduling in Cloud–Fog Computing Environments: A Systematic Literature Review 云雾计算环境中的工作流调度：系统性文献综述

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-22 DOI: 10.1002/cpe.8304

Raouia Bouabdallah, Fairouz Fakhfakh

{"title":"Workflow Scheduling in Cloud–Fog Computing Environments: A Systematic Literature Review","authors":"Raouia Bouabdallah, Fairouz Fakhfakh","doi":"10.1002/cpe.8304","DOIUrl":"https://doi.org/10.1002/cpe.8304","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) facilitates the connectivity of billions of physical devices for exchanging information and enabling a wide range of applications. These applications can be presented in the form of dependent tasks, as outlined in a workflow. These workflows face limitations due to constraints in IoT sensors. To address these limitations, cloud computing has emerged to offer a large capacity of computing and storing with a great capability to adjust resources according to the need. However, cloud computing might not adequately meet the low-latency of IoT workflow requirements when scheduling a workflow composed of IoT tasks due to its centralized nature. Moreover, cloud computing is not ideal for delay-sensitive workflows and may increase communication costs. In response to these challenges, the use of fog computing as an extension to cloud computing scheme is recommended. Fog computing aims to process workflow tasks close to IoT devices. While fog computing offers various advantages, integrating these systems into workflow scheduling remains one of the most formidable challenges in distributed environments. Indeed, significant issues arise due to the timely execution and the resource limitations. In this survey paper, we present a Systematic Literature Review (SLR) on the current state of the art in this domain. We propose a taxonomy to compare and evaluate the existing studies on workflow scheduling approaches in cloud–fog computing environments. This taxonomy encompasses various criteria, including scheduling techniques, performance metrics, workflow dependencies, scheduling policies, and evaluation tools. We highlight certain recommendations for open issues which require more investigations. Our aim is to provide valuable insights for researchers and developers interested in understanding the contributions and challenges of current workflow scheduling approaches in cloud–fog computing environments.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

WSC: A Crowd-Powered Framework for Mapping Decomposable Complex-Task With Worker-Set WSC：众力驱动的可分解复杂任务与工人集映射框架

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-21 DOI: 10.1002/cpe.8305

Suneel Kumar, Sarvesh Pandey

{"title":"WSC: A Crowd-Powered Framework for Mapping Decomposable Complex-Task With Worker-Set","authors":"Suneel Kumar, Sarvesh Pandey","doi":"10.1002/cpe.8305","DOIUrl":"https://doi.org/10.1002/cpe.8305","url":null,"abstract":"<div>\u0000 \u0000 <p>The crowdsourcing platform serves as an intermediary managing the interaction between a requester who posts a decomposable task and a pool of workers who bid to solve it. Each worker intending to take up the task (partially or fully) decomposes it into multiple independent subtasks and submits it to the platform. Selection of a diverse set of workers (based on the bids received) to solve the decomposable task is challenging as it requires balancing factors like cost and quality while encouraging collaboration. We propose a Worker Set Computation (WSC) methodology to address these challenges by selecting a custom set of potential workers who can collaboratively complete the task with the optimal cost, in an efficient way. The aging technique is employed to dynamically update the weight of each worker, giving more weightage to the feedback received in the recent past. This, in turn, not only favors those workers who were rated well in the immediate past but also ensures that one odd feedback does not influence the overall rating heavily. We compare the performance of the proposed method against the state-of-the-art methods, considering the computational (and budget) requirements, as well as the aging-based worker rating.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

3D LVCN: A Lightweight Volumetric ConvNet 3D LVCN：一个轻量级的体积卷积神经网络

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-20 DOI: 10.1002/cpe.8312

Xiaoyun Lu, Chunjie Zhou, Shengjie Liu, Jialong Li

{"title":"3D LVCN: A Lightweight Volumetric ConvNet","authors":"Xiaoyun Lu, Chunjie Zhou, Shengjie Liu, Jialong Li","doi":"10.1002/cpe.8312","DOIUrl":"https://doi.org/10.1002/cpe.8312","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, with the significant increase in the volume of three-dimensional medical image data, three-dimensional medical models have emerged. However, existing methods often require a large number of model parameters to deal with complex medical datasets, leading to high model complexity and significant consumption of computational resources. In order to address these issues, this paper proposes a 3D Lightweight Volume Convolutional Neural Network (3D LVCN), aiming to achieve efficient and accurate volume segmentation. This network architecture combines the design principles of convolutional neural network modules and hierarchical transformers, using large convolutional kernels as the basic framework for feature extraction, while introducing 1 × 1 × 1 convolutional kernels for deep convolution. This improvement not only enhances the computational efficiency of the model but also improves its generalization ability. The pro-posed model is tested on three challenging public datasets, namely spleen, liver, and lung, from the medical segmentation decathlon. Experimental results show that the proposed model performance has in-creased from 0.8315 to 0.8673, with a reduction in parameters of approximately 5%. This indicates that compared to currently advanced model structures, our proposed model architecture exhibits significant advantages in segmentation performance.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Multi-Constrained Green Routing Protocol for IoT-Based Software-Defined WSN 基于物联网的软件定义 WSN 的多约束绿色路由协议

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-17 DOI: 10.1002/cpe.8306

Nitesh Kumar, Rohit Beniwal

{"title":"A Multi-Constrained Green Routing Protocol for IoT-Based Software-Defined WSN","authors":"Nitesh Kumar, Rohit Beniwal","doi":"10.1002/cpe.8306","DOIUrl":"https://doi.org/10.1002/cpe.8306","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent times, there has been a notable surge in the utilization of Internet of Things (IoT) network devices due to their vast applications. However, this rapid growth has undoubtedly led to raised energy consumption, which, in turn, has raised significant concerns about the environment. Consequently, there is a growing demand for green computing techniques that can mitigate IoT device's energy usage and carbon footprint. Clustering IoT networks is a useful strategy for extending their lifespan. However, clustering presents a complex optimization problem that falls under the category of NP-hard; hence making it a challenging issue. Nevertheless, using meta-heuristics algorithms has greatly improved our ability to tackle such challenges. Therefore, this study introduces a clustering scheme called EQ-AHA, which combines Equilibrium optimization and artificial hummingbird optimization techniques to enhance the efficiency of IoT-based Software-Defined Wireless Sensor Networks (IoT-SDWSN). The primary goal of EQ-AHA is to select the Cluster Heads (CHs) and determine the optimal path between CHs and the Base Station (BS). EQ-AHA employs a fitness function that considers three important factors: the distance between CHs, the distance between nodes and the CHs, and the energy levels of the nodes. Overall, this strategy improves the network's performance by 31.6% compared to other State-of-the-Art (SoA) algorithms.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Camellia oleifera trunks detection and identification based on improved YOLOv7 基于改进型 YOLOv7 的油茶树干检测和识别技术

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-17 DOI: 10.1002/cpe.8265

Haorui Wang, Yang Liu, Hong Luo, Yuanyin Luo, Yuyan Zhang, Fei Long, Lijun Li

{"title":"Camellia oleifera trunks detection and identification based on improved YOLOv7","authors":"Haorui Wang, Yang Liu, Hong Luo, Yuanyin Luo, Yuyan Zhang, Fei Long, Lijun Li","doi":"10.1002/cpe.8265","DOIUrl":"https://doi.org/10.1002/cpe.8265","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Camellia oleifera</i> typically thrives in unstructured environments, making the identification of its trunks crucial for advancing agricultural robots towards modernization and sustainability. Traditional target detection algorithms, however, fall short in accurately identifying <i>Camellia oleifera</i> trunks, especially in scenarios characterized by small targets and poor lighting. This article introduces an enhanced trunk detection algorithm for <i>Camellia oleifera</i> based on an improved YOLOv7 model. This model incorporates dynamic snake convolution instead of standard convolutions to bolster its feature extraction capabilities. It integrates more contextual information, thus enhancing the model's generalization ability across various scenes. Additionally, coordinate attention is introduced to refine the model's spatial feature representation, amplifying the network's focus on essential target region features, which in turn boosts detection accuracy and robustness. This feature selectively strengthens response levels across different channels, prioritizing key attributes for classification and localization. Moreover, the original coordinate loss function of YOLOv7 is replaced with EIoU loss, further enhancing the model's robustness and convergence speed. Experimental results demonstrate a recall rate of 96%, a mean average precision (mAP) of 87.9%, an F1 score of 0.87, and a detection speed of 18 milliseconds per frame. When compared with other models like Faster-RCNN, YOLOv3, ScaledYOLOv4, YOLOv5, and the original YOLOv7, our improved model shows mAP increases of 8.1%, 7.0%, 7.5%, and 6.6% respectively. Occupying only 70.8 MB, our model requires 9.8 MB less memory than the original YOLOv7. This model not only achieves high accuracy and detection efficiency but is also easily deployable on mobile devices, providing a robust foundation for future intelligent harvesting technologies.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 27","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enhancing Software-Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q-Learning Approach 利用 Q 学习方法增强软件定义网络的动态负载平衡和容错能力

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-15 DOI: 10.1002/cpe.8298

Ankit Kumar Jain, Pooja Kumari, Rajat Dhull, Krish Jindal, Shahid Raza

{"title":"Enhancing Software-Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q-Learning Approach","authors":"Ankit Kumar Jain, Pooja Kumari, Rajat Dhull, Krish Jindal, Shahid Raza","doi":"10.1002/cpe.8298","DOIUrl":"https://doi.org/10.1002/cpe.8298","url":null,"abstract":"<div>\u0000 \u0000 <p>The Software-Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi-controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in-packet request-response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration-in and migration-out domains by selecting a subset of switches. The architecture utilizes online Q-learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real-world applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach 具有隐私约束的混合云中的作业调度：一种深度强化学习方法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-15 DOI: 10.1002/cpe.8307

Haoyang He, Yan Gu, Qingzhi Liu, Hao Wu, Long Cheng

{"title":"Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach","authors":"Haoyang He, Yan Gu, Qingzhi Liu, Hao Wu, Long Cheng","doi":"10.1002/cpe.8307","DOIUrl":"https://doi.org/10.1002/cpe.8307","url":null,"abstract":"<div>\u0000 \u0000 <p>With the proliferation of cloud computing and the escalating demand for extensive data processing capabilities, an increasing number of enterprises are embracing hybrid cloud solutions. However, as more businesses move toward hybrid clouds, the need for effective solutions to privacy and security concerns becomes increasingly important. Although current scheduling approaches for cloud computing have addressed privacy protection to some extent, few have adequately considered the unique challenges posed by hybrid clouds. To address this gap, we propose a novel approach for scheduling jobs in hybrid clouds that prioritizes privacy protection. Our approach, called PH-DRL, leverages Deep Reinforcement Learning (DRL) to intelligently allocate jobs to virtual machines, optimizing both privacy and Quality of Service (QoS), while minimizing response time. We present the detailed implementation of our approach and our experimental results demonstrate the superior performance of PH-DRL in terms of privacy protection compared to existing methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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