Journal of Cloud Computing-Advances Systems and Applications最新文献

{"title":"AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court","authors":"Jie Song, Haifei Fu, Tianzhe Jiao, Dongqi Wang","doi":"10.1186/s13677-023-00500-z","DOIUrl":"https://doi.org/10.1186/s13677-023-00500-z","url":null,"abstract":"Abstract This paper presents an interesting case study on Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In this study, we propose an AI-enabled LDI method to replace the costly manual approach and ensure privacy protection during the process. We trained AI models to replace tasks such as reading and understanding legacy cases, removing privacy information, composing new case records, and inputting them through the system interfaces. Our approach employs Optical Character Recognition (OCR), text classification, and Named Entity Recognition (NER) to transform legacy data into a system format. We applied our method to a Cloud Arbitration Court in Liaoning Province, China, and achieved a comparable privacy filtering effect while retaining the maximum amount of information. Our method demonstrated similar effectiveness as the manual LDI, but with greater efficiency, saving 90% of the workforce and achieving a 60%-70% information extraction rate compared to manual work. With the increasing development of informationalization and intelligentization in judgment and arbitration, many courts are adopting ABC technologies, namely Artificial intelligence, Big data, and Cloud computing, to build the court system. Our method provides a practical reference for integrating legal data into the system.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Correction to: Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree","authors":"Surjeet Dalal, Umesh Kumar Lilhore, Neetu Faujdar, Sarita Simaiya, Manel Ayadi, Nouf A. Almujally, Amel Ksibi","doi":"10.1186/s13677-023-00526-3","DOIUrl":"https://doi.org/10.1186/s13677-023-00526-3","url":null,"abstract":"","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Resource allocation strategy for blockchain-enabled NOMA-based MEC networks","authors":"Jianjie Ding, Lina Han, Jie Li, Dajun Zhang","doi":"10.1186/s13677-023-00497-5","DOIUrl":"https://doi.org/10.1186/s13677-023-00497-5","url":null,"abstract":"Abstract Blockchain technology is getting more and more attention due to its decentralization, independence and security features. However, in wireless networks it faces a computational challenge: the proof-of-work problem. Mobile edge computing (MEC) leads to a vaild scheme by providing cloud computing capabilities to mobile devices. Non-orthogonal multiple access (NOMA) exploits the diversity properties in the power domain to further increase system throughput and spectral efficiency. In this paper, we suggest a new NOMA-based MEC wireless blockchain network to minimize system energy consumption through task offloading decision optimization, user clustering, computing resource and transmit power allocation. In order to effectively figure out this non-convex problem, we first propose a offloading decision and user clustering algorithm, and then propose a computing resource allocation algorithm based on user Quality of Service (QoS) requirements. Finally, the transmission power can be easily determined. The numerical simulation results verify that the proposed joint optimization algorithm can effectively decrease the system energy consumption.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MapReduce scheduling algorithms in Hadoop: a systematic study","authors":"Soudabeh Hedayati, Neda Maleki, Tobias Olsson, Fredrik Ahlgren, Mahdi Seyednezhad, Kamal Berahmand","doi":"10.1186/s13677-023-00520-9","DOIUrl":"https://doi.org/10.1186/s13677-023-00520-9","url":null,"abstract":"Abstract Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Making programmable packet scheduling time-sensitive with a FIFO queue","authors":"Qianru Lv, Xuyan Jiang, Xiangrui Yang","doi":"10.1186/s13677-023-00518-3","DOIUrl":"https://doi.org/10.1186/s13677-023-00518-3","url":null,"abstract":"Abstract Time-Sensitive Networking (TSN) is an emerging technology for real-time and non-real-time hybrid networked systems. TSN is standardized by IEEE 802.1 TSN Task Group and is becoming widely used in various scenarios including the cloud network. However, existing programmable packet schedulers such as PIFO, PIEO, and AIFO in programmable switches either lack the ability to express most scheduling algorithms in TSN or introduce intolerable on-chip memory overhead (e.g., strict-priority queues). This makes programmable switches and NICs incapable of providing deterministic forwarding. In this paper, we present AIAO (Admission-In-Admission-Out), a new set of programmable scheduling primitives using just a single FIFO to support typical TSN scheduling algorithms, as well as other popular work-conserving algorithms. AIAO is inspired by AIFO but improves it with a group of high-speed packet ingress/egress admission control triggered by high-precise and globally synchronized time, thus being able to support time-sensitive scheduling. We implement AIAO and evaluate it with FPGA-based TSN switches. The preliminary results show that AIAO guarantees correctness for a typical TSN scheduling algorithm with minimal logic and memory overhead.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Privacy and integrity-preserving data aggregation scheme for wireless sensor networks digital twins","authors":"Zhiming Zhang, Wei Yang, Fuying Wu, Ping Li","doi":"10.1186/s13677-023-00522-7","DOIUrl":"https://doi.org/10.1186/s13677-023-00522-7","url":null,"abstract":"Abstract The security technology of digital twin is an important guarantee to ensure the security of digital twin operation, which mainly includes network security technology, data security technology and privacy protection technology. In wireless sensor networks, data aggregation technologies are known as a suitable solution to reduce energy consumption. In addition, due to wireless communications, wireless sensor networks are subject to many attacks. Therefore, it is very important to provide data security in the data aggregation process. In this paper, in order to protect data privacy and verify data integrity, moreover, balance the energy consumption and security during the data aggregation, we present a privacy and integrity–preserving data aggregation scheme for wireless sensor networks based on digital twins technology and homomorphic fingerprinting (HFPIDA). The HFPIDA adopts privacy function to protect data privacy and adopts homomorphic fingerprinting technology to verify the aggregation data integrity. Security analysis shows that the HFPIDA can effectively preserve data privacy and verify data integrity. Simulation results show that the HFPIDA requires less communication and energy overheads, and can achieve higher aggregation accuracy.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud Content Delivery Networks (CDNs)","authors":"M. Sasikumar, P. Jesu Jayarin, F. Sangeetha Francelin Vinnarasi","doi":"10.1186/s13677-023-00519-2","DOIUrl":"https://doi.org/10.1186/s13677-023-00519-2","url":null,"abstract":"Abstract Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier","authors":"S. Rajkumar, Jeevaa Katiravan","doi":"10.1186/s13677-023-00514-7","DOIUrl":"https://doi.org/10.1186/s13677-023-00514-7","url":null,"abstract":"Abstract Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. Intelligent Genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized Intelligent Genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized Intelligent Genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized Intelligent Genetic Load Balancer system performance with existing simulations results and compared the results.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135828505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"HAP-assisted multi-aerial base station deployment for capacity enhancement via federated deep reinforcement learning","authors":"Lei Liu, Haoran He, Fei Qi, Yikun Zhao, Weiliang Xie, Fanqin Zhou, Lei Feng","doi":"10.1186/s13677-023-00512-9","DOIUrl":"https://doi.org/10.1186/s13677-023-00512-9","url":null,"abstract":"Abstract Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are widely employed in cloud computing, disaster relief, and various applications. How to quickly and efficiently deploy multi-AeBSs for higher capacity gain has become a key research issue. In this paper, we address the 3D deployment optimization problem of multi-AeBSs with the objective of maximizing system capacity. To overcome communication overhead and privacy challenges in multi-agent deep reinforcement learning (MADRL), we propose a federated deep deterministic policy gradient (Fed-DDPG) algorithm for the multi-AeBS deployment decision. Specifically, a high-altitude platform (HAP)-assisted multi-AeBS deployment architecture is designed, in which low-altitude AeBS act as the local nodes to train its own deployment decision model, while the HAP acts as the global node to aggregate the weights of local models. In this architecture, AeBSs do not exchange raw data, addressing data privacy concerns and reducing communication overhead. Simulation results show that the proposed algorithm outperforms fully distributed MADRL algorithms and closely approximates the performance of multi-agent deep deterministic policy gradient (MADDPG), which requires global information during training, but with less training time.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree","authors":"Surjeet Dalal, Umesh Kumar Lilhore, Neetu Foujdar, Sarita Simaiya, Manel Ayadi, Nouf A. Almujally, Amel Ksibi","doi":"10.1186/s13677-023-00517-4","DOIUrl":"https://doi.org/10.1186/s13677-023-00517-4","url":null,"abstract":"Abstract Billions of gadgets are already online, making the IoT an essential aspect of daily life. However, the interconnected nature of IoT devices also leaves them open to cyber threats. The quantity and sophistication of cyber assaults aimed against Internet of Things (IoT) systems have skyrocketed in recent years. This paper proposes a next-generation cyber attack prediction framework for IoT systems. The framework uses the multi-class support vector machine (SVM) and the improved CHAID decision tree machine learning methods. IoT traffic is classified using a multi-class support vector machine to identify various types of attacks. The SVM model is then optimized with the help of the CHAID decision tree, which prioritizes the attributes most relevant to the categorization of attacks. The proposed framework was evaluated on a real-world dataset of IoT traffic. The findings demonstrate the framework's ability to categorize attacks accurately. The framework may determine which attributes are most crucial for attack categorization to enhance the SVM model's precision. The proposed technique focuses on network traffic characteristics that can be signs of cybersecurity threats on IoT networks and affected Network nodes. Selected feature vectors were also created utilizing the elements acquired on every IoT console. The evaluation results on the Multistep Cyber-Attack Dataset (MSCAD) show that the proposed CHAID decision tree can significantly predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction is essential in managing cyber attacks in real-time communication. Because of its efficiency and scalability, the model may be used to forecast cyber attacks in real time, even in massive IoT installations. Because of its computing efficiency, it can make accurate predictions rapidly, allowing for prompt detection and action. By locating possible entry points for attacks and mitigating them, the framework helps strengthen the safety of IoT systems.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}