Journal of Cloud Computing最新文献

{"title":"ASOD: an adaptive stream outlier detection method using online strategy","authors":"Zhichao Hu, Xiangzhan Yu, Likun Liu, Yu Zhang, Haining Yu","doi":"10.1186/s13677-024-00682-0","DOIUrl":"https://doi.org/10.1186/s13677-024-00682-0","url":null,"abstract":"In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational intelligence-based classification system for the diagnosis of memory impairment in psychoactive substance users","authors":"Chaoyang Zhu","doi":"10.1186/s13677-024-00675-z","DOIUrl":"https://doi.org/10.1186/s13677-024-00675-z","url":null,"abstract":"Computational intelligence techniques have emerged as a promising approach for diagnosing various medical conditions, including memory impairment. Increased abuse of psychoactive drugs poses a global public health burden, as repeated exposure to these substances can cause neurodegeneration, premature aging, and negatively affect memory impairment. Many studies in the literature relied on statistical studies, but they remained inaccurate. Some studies relied on physical data because the time factor was not considered, until Artificial Intelligence (AI) techniques came along that proved their worth in this diagnosis. The variable deep neural network method was used to adapt to the intermediate results and re-process the intermediate in case the result is undesirable. Computational intelligence was used in this study to classify a brain image from MRI or CT scans and to show the effectiveness of the dose ratio on health with treatment time, and to diagnose memory impairment in users of psychoactive substances. Understanding the neurotoxic profiles of psychoactive substances and the underlying pathways is hypothesized to be of great importance in improving the risk assessment and treatment of substance use disorders. The results proved the worth of the proposed method in terms of the accuracy of recognition rate as well as the possibility of diagnosis. It can be concluded that the diagnostic efficiency is increased by increasing the number of hidden layers in the neural network and controlling the weights and variables that control the deep learning algorithm. Thus, we conclude that good classification in this field may save human life or early detection of memory impairment.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive scheduling-based fine-grained greybox fuzzing for cloud-native applications","authors":"Jiageng Yang, Chuanyi Liu, Binxing Fang","doi":"10.1186/s13677-024-00681-1","DOIUrl":"https://doi.org/10.1186/s13677-024-00681-1","url":null,"abstract":"Coverage-guided fuzzing is one of the most popular approaches to detect bugs in programs. Existing work has shown that coverage metrics are a crucial factor in guiding fuzzing exploration of targets. A fine-grained coverage metric can help fuzzing to detect more bugs and trigger more execution states. Cloud-native applications that written by Golang play an important role in the modern computing paradigm. However, existing fuzzers for Golang still employ coarse-grained block coverage metrics, and there is no fuzzer specifically for cloud-native applications, which hinders the bug detection in cloud-native applications. Using fine-grained coverage metrics introduces more seeds and even leads to seed explosion, especially in large targets such as cloud-native applications. Therefore, we employ an accurate edge coverage metric in fuzzer for Golang, which achieves finer test granularity and more accurate coverage information than block coverage metrics. To mitigate the seed explosion problem caused by fine-grained coverage metrics and large target sizes, we propose smart seed selection and adaptive task scheduling algorithms based on a variant of the classical adversarial multi-armed bandit (AMAB) algorithm. Extensive evaluation of our prototype on 16 targets in real-world cloud-native infrastructures shows that our approach detects 233% more bugs than go-fuzz, achieving an average coverage improvement of 100.7%. Our approach effectively mitigates seed explosion by reducing the number of seeds generated by 41% and introduces only 14% performance overhead.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-orthogonal multiple access-based MEC for energy-efficient task offloading in e-commerce systems","authors":"Xiao Zheng, Muhammad Tahir, Khursheed Aurangzeb, Muhammad Shahid Anwar, Muhammad Aamir, Ahmad Farzan, Rizwan Ullah","doi":"10.1186/s13677-024-00680-2","DOIUrl":"https://doi.org/10.1186/s13677-024-00680-2","url":null,"abstract":"Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple time servers timed-release encryption based on Shamir secret sharing for EHR cloud system","authors":"Ke Yuan, Ziwei Cheng, Keyan Chen, Bozhen Wang, Junyang Sun, Sufang Zhou, Chunfu Jia","doi":"10.1186/s13677-024-00676-y","DOIUrl":"https://doi.org/10.1186/s13677-024-00676-y","url":null,"abstract":"Electronic health record (EHR) cloud system, as a primary tool driving the informatization of medical data, have positively impacted both doctors and patients by providing accurate and complete patient information. However, ensuring the security of EHR cloud system remains a critical issue. Some patients require regular remote medical services, and controlling access to medical data involving patient privacy during specific times is essential. Timed-release encryption (TRE) technology enables the sender to preset a future time T at which the data can be decrypted and accessed. It is a cryptographic primitive with time-dependent properties. Currently, mainstream TRE schemes are based on non-interactive single time server methods. However, if the single time server is attacked or corrupted, it is easy to directly threaten the security applications of TRE. Although some research schemes “distribute” the single time server into multiple ones, they still cannot resist the single point of failure problem. To address this issue, we propose a multiple time servers TRE scheme based on Shamir secret sharing and another variant derived from it. In our proposed schemes, the data receiver does not need to interact with the time servers; instead, they only need to obtain the time trapdoors that exceed or equal the preset threshold value for decryption, which ensures the identity privacy of the data sender and tolerates partial downtime or other failures of some time servers, significantly improving TRE reliability. Security analysis indicates that our proposed schemes demonstrate data confidentiality, verifiability, anti-advance decryption, and robust decryption with multiple time trapdoors, making them more practical. Efficiency analysis indicates that although our schemes have slightly higher computational costs than most efficient existing TRE schemes, such differences are insignificant from a practical application perspective.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework","authors":"Hongxia He, Xi Li, Peng Chen, Juan Chen, Ming Liu, Lei Wu","doi":"10.1186/s13677-024-00677-x","DOIUrl":"https://doi.org/10.1186/s13677-024-00677-x","url":null,"abstract":"Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From data to insights: the application and challenges of knowledge graphs in intelligent audit","authors":"Hao Zhong, Dong Yang, Shengdong Shi, Lai Wei, Yanyan Wang","doi":"10.1186/s13677-024-00674-0","DOIUrl":"https://doi.org/10.1186/s13677-024-00674-0","url":null,"abstract":"In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HybOff: a Hybrid Offloading approach to improve load balancing in fog environments","authors":"Hamza Sulimani, Rahaf Sulimani, Fahimeh Ramezani, Mohsen Naderpour, Huan Huo, Tony Jan, Mukesh Prasad","doi":"10.1186/s13677-024-00663-3","DOIUrl":"https://doi.org/10.1186/s13677-024-00663-3","url":null,"abstract":"Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Identity-Based Authenticated Encryption with Keyword Search over encrypted cloud data","authors":"Danial Shiraly, Ziba Eslami, Nasrollah Pakniat","doi":"10.1186/s13677-024-00633-9","DOIUrl":"https://doi.org/10.1186/s13677-024-00633-9","url":null,"abstract":"With the rapid development of cloud computing technology, cloud storage services are becoming more and more mature. However, the storage of sensitive data on remote servers poses privacy risks and is presently a source of concern. Searchable Encryption (SE) is an effective method for protecting sensitive data while preserving server-side searchability. Hierarchical Public key Encryption with Keyword Search (HPEKS), a new variant of SE, allows users with higher access permission to search over encrypted data sent to lower-level users. To the best of our knowledge, there exist only four HPEKS schemes in the literature. Two of them are in traditional public-key setting, and the remaining ones are identity-based public key cryptosystems. Unfortunately, all of the four existing HPEKS schemes are vulnerable against inside Keyword Guessing Attacks (KGAs). Moreover, all of the existing HPEKS schemes are based on the computationally expensive bilinear pairing operation which dramatically increases the computational costs. To overcome these issues, in this paper, we introduce the notion of Hierarchical Identity-Based Authenticated Encryption with Keyword Search (HIBAEKS). We formulate a security model for HIBAEKS and propose an efficient pairing-free HIBAEKS scheme. We then prove that the proposed HIBAEKS scheme is secure under the defined security model and is resistant against KGAs. Finally, we compare our proposed scheme with related constructions regarding security requirements, computational and communication costs to indicate the overall superiority of our proposed scheme.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141171546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN","authors":"Chengping Zhang, Muhammad Aamir, Yurong Guan, Muna Al-Razgan, Emad Mahrous Awwad, Rizwan Ullah, Uzair Aslam Bhatti, Yazeed Yasin Ghadi","doi":"10.1186/s13677-024-00673-1","DOIUrl":"https://doi.org/10.1186/s13677-024-00673-1","url":null,"abstract":"<p>Following publication of the original article [1], we have been notified that Acknowledgement declaration was published incorrectly.</p><p>It is now:</p><p>Acknowledgements</p><p>The authors express their gratitude to Huanggang Normal University for supporting this research. Furthermore, they acknowledge the support from King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Program number (RSPD2024R206).</p><p>It should be as per below:</p><p>Acknowledgements</p><p>The authors express their gratitude to Huanggang Normal University for supporting this research. Furthermore, they acknowledge the support from King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Program number (RSP2024R206).</p><ol data-track-component=\"outbound reference\"><li data-counter=\"1.\"><p>Zhang, CNN (2024) Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and (2024). 17:13 https://doi.org/10.1186/s13677-024-00597-w</p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><p>The authors express their gratitude to Huanggang Normal University for supporting this research. Furthermore, they acknowledge the support from King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Program number (RSP2024R206).</p><h3>Authors and Affiliations</h3><ol><li><p>Mechanical and Electrical Engineering College, Hainan Vocational University of Science and Technology, Haikou, 571126, China</p><p>Chengping Zhang</p></li><li><p>College of Computer Science, Huanggang Normal University, Huanggang, 438000, China</p><p>Muhammad Aamir &amp; Yurong Guan</p></li><li><p>Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 22452, Riyadh, 11495, Saudi Arabia</p><p>Muna Al-Razgan</p></li><li><p>Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia</p><p>Emad Mahrous Awwad</p></li><li><p>Faculty of Engineering, Chulalongkorn University Bangkok Thailand, Bangkok, Thailand</p><p>Rizwan Ullah</p></li><li><p>School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China</p><p>Uzair Aslam Bhatti</p></li><li><p>Department of Computer Science, Al Ain University, Al Ain, UAE</p><p>Yazeed Yasin Ghadi</p></li></ol><span>Authors</span><ol><li><span>Chengping Zhang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Muhammad Aamir</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Yurong Guan</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Muna ","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141171545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}