Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00022
Bolin Yang, Fan Zhang, S. Khan
Elasticity is one of the most important cloud computing characteristics, which enables deployed applications to dynamically adapt to workload-changing demands by acquiring and releasing shared computing resources at runtime. However, the existing cloud elasticity metrics are either oversimplified or hard to use, thereby lacking a comprehensive evaluation mech-anism to properly compare the elastic feature among different cloud providers. To address this gap, we propose an assessment method for cloud elasticity based on fuzzy hierarchical analysis. We use a fuzzy hierarchical model to quantitatively assess the qualitative metrics with a unified standard model. We compare three public cloud providers (Ali Cloud, HUAWEI Cloud, Tencent Cloud) as case studies and measure their cloud elasticity based on the proposed model on a cluster. To verify the effectiveness of our method, we also measure three cloud platforms using auto scaling performance metrics proposed by SPEC Cloud Group. The results show that our proposed elasticity quantification method is feasible.
{"title":"Quantitative Evaluation of Cloud Elasticity based on Fuzzy Analytic Hierarchy Process","authors":"Bolin Yang, Fan Zhang, S. Khan","doi":"10.1109/CloudSummit54781.2022.00022","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00022","url":null,"abstract":"Elasticity is one of the most important cloud computing characteristics, which enables deployed applications to dynamically adapt to workload-changing demands by acquiring and releasing shared computing resources at runtime. However, the existing cloud elasticity metrics are either oversimplified or hard to use, thereby lacking a comprehensive evaluation mech-anism to properly compare the elastic feature among different cloud providers. To address this gap, we propose an assessment method for cloud elasticity based on fuzzy hierarchical analysis. We use a fuzzy hierarchical model to quantitatively assess the qualitative metrics with a unified standard model. We compare three public cloud providers (Ali Cloud, HUAWEI Cloud, Tencent Cloud) as case studies and measure their cloud elasticity based on the proposed model on a cluster. To verify the effectiveness of our method, we also measure three cloud platforms using auto scaling performance metrics proposed by SPEC Cloud Group. The results show that our proposed elasticity quantification method is feasible.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121268384","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00014
Kai Cui, Guoting Zhang, Fan Zhang, S. Khan
Time-sensitive AI applications usually pre-process the raw data on edge devices without having to offload them all to the cloud. However, deploying the AI applications on a distributed edge-cloud infrastructure is still an open issue since separating the roles between the edge and the cloud has no existing rule to follow. In this paper, we implemented a Facial Expression Recognition (FER) system, as a case study AI application, on an edge-cloud infrastructure to bridge the gap. FER system is distributed, fault tolerant, performant and completely edge-cloud separated. FER performs light-weight algorithms such as extracting facial feature points on the edge, while it performs heavy-weight algorithms such as deep neural network inference on the cloud. We performed experiments on different cloud providers, and we have seen that we reduced the network overhead significantly and improved the performance by 25% compared with deploying it solely on the cloud, with only the feature data being transferred to the cloud instead of all the raw data.
{"title":"Facial Expression Recognition System on a Distributed Edge-Cloud Infrastructure","authors":"Kai Cui, Guoting Zhang, Fan Zhang, S. Khan","doi":"10.1109/CloudSummit54781.2022.00014","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00014","url":null,"abstract":"Time-sensitive AI applications usually pre-process the raw data on edge devices without having to offload them all to the cloud. However, deploying the AI applications on a distributed edge-cloud infrastructure is still an open issue since separating the roles between the edge and the cloud has no existing rule to follow. In this paper, we implemented a Facial Expression Recognition (FER) system, as a case study AI application, on an edge-cloud infrastructure to bridge the gap. FER system is distributed, fault tolerant, performant and completely edge-cloud separated. FER performs light-weight algorithms such as extracting facial feature points on the edge, while it performs heavy-weight algorithms such as deep neural network inference on the cloud. We performed experiments on different cloud providers, and we have seen that we reduced the network overhead significantly and improved the performance by 25% compared with deploying it solely on the cloud, with only the feature data being transferred to the cloud instead of all the raw data.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124238","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00016
X. Merino, Carlos E. Otero
Containerization has enabled applications to be deployed in ever-changing environments, restarted, updated, migrated, and frequently rolled back to earlier versions. Because host placement and scheduling are not guaranteed, an application may be restarted in a different host or at a later time, losing its sense of time and refusing service owing to incongruent states or network timeouts. Until now, process time was determined by the host. The most recent Linux time namespace allows for per-service timelines, regardless of the host. Because container engines do not yet support the time namespace, we offer a workflow for creating time-aware containers, as well as the first performance analysis of virtualizing time in Linux containers using this namespace. We consider 11 time-related system calls and their vDSO variants, making this one of the most comprehensive studies on the overhead of time virtualization in the literature. Our findings show that time virtualization adds 2-4% overhead, in line with current containerization overhead.
{"title":"The Cost of Virtualizing Time in Linux Containers","authors":"X. Merino, Carlos E. Otero","doi":"10.1109/CloudSummit54781.2022.00016","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00016","url":null,"abstract":"Containerization has enabled applications to be deployed in ever-changing environments, restarted, updated, migrated, and frequently rolled back to earlier versions. Because host placement and scheduling are not guaranteed, an application may be restarted in a different host or at a later time, losing its sense of time and refusing service owing to incongruent states or network timeouts. Until now, process time was determined by the host. The most recent Linux time namespace allows for per-service timelines, regardless of the host. Because container engines do not yet support the time namespace, we offer a workflow for creating time-aware containers, as well as the first performance analysis of virtualizing time in Linux containers using this namespace. We consider 11 time-related system calls and their vDSO variants, making this one of the most comprehensive studies on the overhead of time virtualization in the literature. Our findings show that time virtualization adds 2-4% overhead, in line with current containerization overhead.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133309646","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00010
R.-C. Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez, A. Iosup, E. Kharlamov, Jože M. Rožanec, Laurentiu A. Vasiliu, A. Varbanescu
The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.
{"title":"Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe","authors":"R.-C. Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez, A. Iosup, E. Kharlamov, Jože M. Rožanec, Laurentiu A. Vasiliu, A. Varbanescu","doi":"10.1109/CloudSummit54781.2022.00010","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00010","url":null,"abstract":"The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123228277","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00019
Razieh Abbasi Ghalehtaki, Amin Ebrahimzadeh, R. Glitho
Machine learning is expected to play an instrumental role in automating the detection of faults in next-generation cloud networks. The existing machine-learning-based fault detection methods suffer from the following drawbacks: (i) ignoring the issue of missing feature values, (ii) ignoring the impact of each feature on output prediction over other features (measurement of feature importance), and (iii) lack of calculation of the proper number of features for fault detection. To address the above challenges, in this paper, we propose a context-aware feature selection method to improve the performance of fault detection methods in the cloud environment, aiming at maximizing the $F_{1}$-score. Our proposed solution comprises Denoising Auto-Encoder (DAE) stacked with a Discriminative Model (DM). The DAE is applied to handle the missing feature values and encoding features while the DM is responsible for making predictions of system status based on the encoded features. Then, the sensitivity analysis of output prediction with respect to each input feature value is used to measure the feature importance. We compare our work with existing solutions from the literature. Our results reveal that the proposed solution can improve the $F_{1}$-score up to 47 % and 76 % in the scenario where all feature values are known and in the scenario where only 25 % of feature values are known, respectively.
{"title":"Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments","authors":"Razieh Abbasi Ghalehtaki, Amin Ebrahimzadeh, R. Glitho","doi":"10.1109/CloudSummit54781.2022.00019","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00019","url":null,"abstract":"Machine learning is expected to play an instrumental role in automating the detection of faults in next-generation cloud networks. The existing machine-learning-based fault detection methods suffer from the following drawbacks: (i) ignoring the issue of missing feature values, (ii) ignoring the impact of each feature on output prediction over other features (measurement of feature importance), and (iii) lack of calculation of the proper number of features for fault detection. To address the above challenges, in this paper, we propose a context-aware feature selection method to improve the performance of fault detection methods in the cloud environment, aiming at maximizing the $F_{1}$-score. Our proposed solution comprises Denoising Auto-Encoder (DAE) stacked with a Discriminative Model (DM). The DAE is applied to handle the missing feature values and encoding features while the DM is responsible for making predictions of system status based on the encoded features. Then, the sensitivity analysis of output prediction with respect to each input feature value is used to measure the feature importance. We compare our work with existing solutions from the literature. Our results reveal that the proposed solution can improve the $F_{1}$-score up to 47 % and 76 % in the scenario where all feature values are known and in the scenario where only 25 % of feature values are known, respectively.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592611","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00007
Antonio Cortés Castillo
The exponential rise of servers in the cloud generates the need for network topologies for specialized data centers, which means that the service requirements are accompanied by the availability of massive storage and quality of services, essential aspects for handling large volumes of data in large server farms located in the DCN. In turn, the requirements for new cloud services have grown exponentially, so DCNs face new challenges related to scalability, energy efficiency, network congestion, and cost, which are directly associated with the architectures and DCN network topologies. Similarly, from existing network topologies we propose a DCN topology. In this paper, Fat-Tree and BCube network topologies are compared by considering the architectures themselves, the metrics for comparing various topology types, and two statistical functions are used such as the exponential random traffic distribution and uniform random traffic distribution. This type of comparison helps to solve the existing problem regarding the demand for new services, load balance, bandwidth, and node requirements in a DC network infrastructure.
{"title":"Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview","authors":"Antonio Cortés Castillo","doi":"10.1109/CloudSummit54781.2022.00007","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00007","url":null,"abstract":"The exponential rise of servers in the cloud generates the need for network topologies for specialized data centers, which means that the service requirements are accompanied by the availability of massive storage and quality of services, essential aspects for handling large volumes of data in large server farms located in the DCN. In turn, the requirements for new cloud services have grown exponentially, so DCNs face new challenges related to scalability, energy efficiency, network congestion, and cost, which are directly associated with the architectures and DCN network topologies. Similarly, from existing network topologies we propose a DCN topology. In this paper, Fat-Tree and BCube network topologies are compared by considering the architectures themselves, the metrics for comparing various topology types, and two statistical functions are used such as the exponential random traffic distribution and uniform random traffic distribution. This type of comparison helps to solve the existing problem regarding the demand for new services, load balance, bandwidth, and node requirements in a DC network infrastructure.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646588","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00021
Aritra Ray, Zhaobo Zhang, Ying Xiong, K. Chakrabarty
Machine Learning (ML) jobs significantly benefit when trained on abundant GPU resources. It leads to resource contention when several ML training jobs are scheduled con-currently on a single GPU in the compute cluster. A job's performance is susceptible to its competitor's task on a single GPU. We, in this paper, propose PriRecT, a novel ML job recommendation tool that preserves user privacy for scheduling ML training jobs in the GPU compute cluster. We perform workload characterization for several ML training scripts, and the Futurewei mini-ML Workload Dataset is released publicly [1]. We build a knowledge base of inter and intra-cluster task interference for GPU sharing through a clustering-based approach. For scheduling purposes, PriRecT blinds the user-sensitive information and assigns the job to an existing cluster. Based on clustering results, PriRecT recommends jobs that should run concurrently on a single GPU to minimize task interference and additionally assigns an uncertainty score to account for job variations in the recommendation.
{"title":"PriRecT: Privacy-preserving Job Recommendation Tool for GPU Sharing","authors":"Aritra Ray, Zhaobo Zhang, Ying Xiong, K. Chakrabarty","doi":"10.1109/CloudSummit54781.2022.00021","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00021","url":null,"abstract":"Machine Learning (ML) jobs significantly benefit when trained on abundant GPU resources. It leads to resource contention when several ML training jobs are scheduled con-currently on a single GPU in the compute cluster. A job's performance is susceptible to its competitor's task on a single GPU. We, in this paper, propose PriRecT, a novel ML job recommendation tool that preserves user privacy for scheduling ML training jobs in the GPU compute cluster. We perform workload characterization for several ML training scripts, and the Futurewei mini-ML Workload Dataset is released publicly [1]. We build a knowledge base of inter and intra-cluster task interference for GPU sharing through a clustering-based approach. For scheduling purposes, PriRecT blinds the user-sensitive information and assigns the job to an existing cluster. Based on clustering results, PriRecT recommends jobs that should run concurrently on a single GPU to minimize task interference and additionally assigns an uncertainty score to account for job variations in the recommendation.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121267399","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}
Pub Date : 2022-10-01DOI: 10.1109/CloudSummit54781.2022.00013
J. Akoto, Tara Salman
Detecting intrusions is a critical issue in cyberse-curity. One way to overcome this issue is to build efficient and robust Network Intrusion Detection Systems (NIDS) using existing Machine Learning (ML) algorithms. Such an approach has been proposed in the literature and has been shown to perform well. However, a comparative analysis of the performance of ML and Deep Learning (DL) based NIDS for both detection and categorization of intrusions is still needed. This paper investigates the performance of ML and DL models for both intrusion detection and categorization. We use the publicly available Canadian Institute of Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset to train and test ML and DL models. We apply three traditional ML models, namely, Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and three DL models − 1-D Convolutional Neural Network (ConvlD), Recurrent Neural Network (RNN), and a two-staged model that combines an unsupervised Dense Autoencoders (DAE) for pre-training and an Artificial Neural Network (ANN) for classification. Our results demonstrate that RF is the best performing ML model with a detection accuracy of 99.5% and DAE-ANN is the best performing DL model with a detection accuracy of 98.7%. We also show the advantages of using a stepwise multi-classification over a classical single-stage multi-classification. Finally, we observe that RF outperforms DAE-ANN in categorization with detection rates of 91.35 % and 84.66 %, respectively.
{"title":"Machine Learning vs Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments","authors":"J. Akoto, Tara Salman","doi":"10.1109/CloudSummit54781.2022.00013","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00013","url":null,"abstract":"Detecting intrusions is a critical issue in cyberse-curity. One way to overcome this issue is to build efficient and robust Network Intrusion Detection Systems (NIDS) using existing Machine Learning (ML) algorithms. Such an approach has been proposed in the literature and has been shown to perform well. However, a comparative analysis of the performance of ML and Deep Learning (DL) based NIDS for both detection and categorization of intrusions is still needed. This paper investigates the performance of ML and DL models for both intrusion detection and categorization. We use the publicly available Canadian Institute of Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset to train and test ML and DL models. We apply three traditional ML models, namely, Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and three DL models − 1-D Convolutional Neural Network (ConvlD), Recurrent Neural Network (RNN), and a two-staged model that combines an unsupervised Dense Autoencoders (DAE) for pre-training and an Artificial Neural Network (ANN) for classification. Our results demonstrate that RF is the best performing ML model with a detection accuracy of 99.5% and DAE-ANN is the best performing DL model with a detection accuracy of 98.7%. We also show the advantages of using a stepwise multi-classification over a classical single-stage multi-classification. Finally, we observe that RF outperforms DAE-ANN in categorization with detection rates of 91.35 % and 84.66 %, respectively.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125946578","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}
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