A power-aware job scheduling algorithm is proposed to increase the energy efficiency of high performance computer by selectively hibernating some idle nodes during periods of low load. The proposed algorithm records the switching logs of nodes' power states, dynamic setting idle nodes to sleeping status. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed algorithm can achieve effective energy savings with good control on the switching frequency of nodes'power states.
{"title":"A Power-aware Job Scheduling Algorithm","authors":"Haitao Chen, Yutong Lu, Qinghua Zhu","doi":"10.1109/CSC.2012.9","DOIUrl":"https://doi.org/10.1109/CSC.2012.9","url":null,"abstract":"A power-aware job scheduling algorithm is proposed to increase the energy efficiency of high performance computer by selectively hibernating some idle nodes during periods of low load. The proposed algorithm records the switching logs of nodes' power states, dynamic setting idle nodes to sleeping status. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed algorithm can achieve effective energy savings with good control on the switching frequency of nodes'power states.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122686258","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}
Gansen Zhao, Ziliu Li, Wen J. Li, H. Zhang, Yong Tang
Platform as a service (PaaS) is a cloud computing service model that provides a computing platform and a solution stack as an on-demand service, allowing users to create, deploy and control their own cloud services without building and managing their own computing platforms. PaaS providers provide the networks, servers and storages alongside with the PaaS platform. Though cloud computing is getting prevalence in IT, security, in particular privacy, has incurred the most concerns from users. Using a cloud service, a user has no privilege in managing the underlying computing platform nor the underlying resource and infrastructure, leading to the situation that a user has no way to monitor cloud services' behavior to protect the user's privacy. This paper proposes a privacy enhancing framework on PaaS for detecting the privacy violating behavior and protecting user's information instantly by enforcing related protection actions. The proposed framework allows customized security policies and behavior analysis models, enabling users to impose application oriented privacy monitoring mechanisms.
{"title":"Privacy Enhancing Framework on PaaS","authors":"Gansen Zhao, Ziliu Li, Wen J. Li, H. Zhang, Yong Tang","doi":"10.1109/CSC.2012.27","DOIUrl":"https://doi.org/10.1109/CSC.2012.27","url":null,"abstract":"Platform as a service (PaaS) is a cloud computing service model that provides a computing platform and a solution stack as an on-demand service, allowing users to create, deploy and control their own cloud services without building and managing their own computing platforms. PaaS providers provide the networks, servers and storages alongside with the PaaS platform. Though cloud computing is getting prevalence in IT, security, in particular privacy, has incurred the most concerns from users. Using a cloud service, a user has no privilege in managing the underlying computing platform nor the underlying resource and infrastructure, leading to the situation that a user has no way to monitor cloud services' behavior to protect the user's privacy. This paper proposes a privacy enhancing framework on PaaS for detecting the privacy violating behavior and protecting user's information instantly by enforcing related protection actions. The proposed framework allows customized security policies and behavior analysis models, enabling users to impose application oriented privacy monitoring mechanisms.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130302917","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}
Web and service applications are generally I/O bound and follow a Zipf-like request distribution, ushering in potential for significant latency reduction by caching and reusing results. However, such web caches require manual resource allocation, and when deployed in the cloud, costs may further complicate the provisioning process. We propose a fully autonomous, self-scaling, and cost-aware cloud cache with the objective of accelerating data-intensive applications. Our system, which is distributed over multiple cloud nodes, intelligently provisions resources at runtime based on user's cost and performance expectations, while abstracting the various low-level decisions regarding efficient cloud resource management and data placement within the cloud from the user. Our prediction model lends the system the capability to auto-configure the optimal resource requirement to automatically scale itself up (or down) to accommodate demand peaks while staying within certain cost constraints while fulfilling the performance expectations. Our evaluation shows a 5.5 time speedup for a typical web workload, while staying under cost constraints.
{"title":"Reconciling Cost and Performance Objectives for Elastic Web Caches","authors":"Farhana Kabir, David Chiu","doi":"10.1109/CSC.2012.21","DOIUrl":"https://doi.org/10.1109/CSC.2012.21","url":null,"abstract":"Web and service applications are generally I/O bound and follow a Zipf-like request distribution, ushering in potential for significant latency reduction by caching and reusing results. However, such web caches require manual resource allocation, and when deployed in the cloud, costs may further complicate the provisioning process. We propose a fully autonomous, self-scaling, and cost-aware cloud cache with the objective of accelerating data-intensive applications. Our system, which is distributed over multiple cloud nodes, intelligently provisions resources at runtime based on user's cost and performance expectations, while abstracting the various low-level decisions regarding efficient cloud resource management and data placement within the cloud from the user. Our prediction model lends the system the capability to auto-configure the optimal resource requirement to automatically scale itself up (or down) to accommodate demand peaks while staying within certain cost constraints while fulfilling the performance expectations. Our evaluation shows a 5.5 time speedup for a typical web workload, while staying under cost constraints.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124346088","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}
M. Beraka, H. Mathkour, Sofien Gannouni, H. Hashimi
Web services are loose-coupled, which allows developers to create, generate and compose them at runtime. But, single Web service isn't sufficient to achieve most of user demand in the current period. Therefore, composition of Web services is the appropriate solution and it's the best way to deal with various user requests. It has received a great attention from different communities. A number of different standards/specifications have been proposed to tackle this issue. These standards are Ontology Web Language, Web Service Modeling Ontology, Business Process Modeling Language, Web Services Business Process Execution Language, Web Service Choreography Interface and Web Service -- Choreography Description Language. In this paper, we provide an overview of different applications that have been developed based on each of these standards, and present three comparisons between those applications along with the popularity of Web search for them.
{"title":"Applications of Different Web Service Composition Standards","authors":"M. Beraka, H. Mathkour, Sofien Gannouni, H. Hashimi","doi":"10.1109/CSC.2012.17","DOIUrl":"https://doi.org/10.1109/CSC.2012.17","url":null,"abstract":"Web services are loose-coupled, which allows developers to create, generate and compose them at runtime. But, single Web service isn't sufficient to achieve most of user demand in the current period. Therefore, composition of Web services is the appropriate solution and it's the best way to deal with various user requests. It has received a great attention from different communities. A number of different standards/specifications have been proposed to tackle this issue. These standards are Ontology Web Language, Web Service Modeling Ontology, Business Process Modeling Language, Web Services Business Process Execution Language, Web Service Choreography Interface and Web Service -- Choreography Description Language. In this paper, we provide an overview of different applications that have been developed based on each of these standards, and present three comparisons between those applications along with the popularity of Web search for them.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251723","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}
It is a challenge to recommend Web services under multiple contexts. To address this challenge, we propose a context-aware collaborative filtering (CaCF) approach for service recommendation. Three types of contextual information, i.e. time, location and interest of user, are considered. In this approach, users' interests are extracted from service invocation records and represented as term-weight vectors. Neighbors are chosen according to the Cosine similarities of these vectors. Then, neighbors are filtered into close neighbors by location and time. At last, these close neighbors recommend service to a target user. We evaluate our method through comparing with other service recommendation approaches. The experimental results show that it achieves better precision and satisfaction rate than other two methods.
{"title":"A context-aware collaborative filtering approach for service recommendation","authors":"Rong Hu, Wanchun Dou, Jianxun Liu","doi":"10.1109/CSC.2012.30","DOIUrl":"https://doi.org/10.1109/CSC.2012.30","url":null,"abstract":"It is a challenge to recommend Web services under multiple contexts. To address this challenge, we propose a context-aware collaborative filtering (CaCF) approach for service recommendation. Three types of contextual information, i.e. time, location and interest of user, are considered. In this approach, users' interests are extracted from service invocation records and represented as term-weight vectors. Neighbors are chosen according to the Cosine similarities of these vectors. Then, neighbors are filtered into close neighbors by location and time. At last, these close neighbors recommend service to a target user. We evaluate our method through comparing with other service recommendation approaches. The experimental results show that it achieves better precision and satisfaction rate than other two methods.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849657","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}
One common feature in SaaS (Software as a Service) business model is that the customer's private business data is hosted by the service provider. This causes a problem to SMB (Small and Medium Business) consumers: it is hard to guarantee the confidentiality of theirs private business data. This paper presents a solution to this problem: the customer's private business data are stored and protected in SMB side, and the business data is guaranteed visible only to the customer itself. Smart MSBG (Multi-Service Business Gateway) is introduced in this paper. Smart MSBG and Service Management Center are two main components to implement the service management. Every SMB customer hosts one Smart MSBG. The Smart MSBG is used to store the SMB customer private business data at customer side. The Management Center is used to manage all Smart MSBGs at service provider side. Service management operations including service subscription, service delivery, and service upgrade, service availability management can be performed through the coordination between Management Center and Smart MSBG. In this paper, we first studied the problem in current SaaS business model, and then we presented the architecture of our solution and test bed. We also described the structure of Management Center and Smart MSBG, as well as how they work together in typical service management scenarios.
{"title":"One Solution to Improve the Confidentiality of Customer's Private Business Data in SaaS Model","authors":"L. Yi, Kai X. Miao","doi":"10.1109/CSC.2012.28","DOIUrl":"https://doi.org/10.1109/CSC.2012.28","url":null,"abstract":"One common feature in SaaS (Software as a Service) business model is that the customer's private business data is hosted by the service provider. This causes a problem to SMB (Small and Medium Business) consumers: it is hard to guarantee the confidentiality of theirs private business data. This paper presents a solution to this problem: the customer's private business data are stored and protected in SMB side, and the business data is guaranteed visible only to the customer itself. Smart MSBG (Multi-Service Business Gateway) is introduced in this paper. Smart MSBG and Service Management Center are two main components to implement the service management. Every SMB customer hosts one Smart MSBG. The Smart MSBG is used to store the SMB customer private business data at customer side. The Management Center is used to manage all Smart MSBGs at service provider side. Service management operations including service subscription, service delivery, and service upgrade, service availability management can be performed through the coordination between Management Center and Smart MSBG. In this paper, we first studied the problem in current SaaS business model, and then we presented the architecture of our solution and test bed. We also described the structure of Management Center and Smart MSBG, as well as how they work together in typical service management scenarios.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132401198","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}
Jun Xu, Weiming Zhang, Ce Yang, Jiajia Xu, Nenghai Yu
To protect privacy of users, sensitive data need to be encrypted before outsourcing to cloud, which makes effective data retrieval a very tough task. In this paper, we proposed a novel order-preserving encryption(OPE) based ranked search scheme over encrypted cloud data, which uses the encrypted keyword frequency to rank the results and provide accurate results via two-step ranking strategy. The first step coarsely ranks the documents with the measure of coordinate matching, i.e., classifying the documents according to the number of query terms included in each document. In the second step, for each category obtained in the first step, a fine ranking process is executed by adding up the encrypted score. Extensive experiments show that this new method is indeed an advanced solution for secure multi-keyword retrieval.
{"title":"Two-Step-Ranking Secure Multi-Keyword Search over Encrypted Cloud Data","authors":"Jun Xu, Weiming Zhang, Ce Yang, Jiajia Xu, Nenghai Yu","doi":"10.1109/CSC.2012.26","DOIUrl":"https://doi.org/10.1109/CSC.2012.26","url":null,"abstract":"To protect privacy of users, sensitive data need to be encrypted before outsourcing to cloud, which makes effective data retrieval a very tough task. In this paper, we proposed a novel order-preserving encryption(OPE) based ranked search scheme over encrypted cloud data, which uses the encrypted keyword frequency to rank the results and provide accurate results via two-step ranking strategy. The first step coarsely ranks the documents with the measure of coordinate matching, i.e., classifying the documents according to the number of query terms included in each document. In the second step, for each category obtained in the first step, a fine ranking process is executed by adding up the encrypted score. Extensive experiments show that this new method is indeed an advanced solution for secure multi-keyword retrieval.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921152","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}
As a fundamental building block of smart cities, smart infrastructure has been increasingly drawing attention in both academia and industry across the globe. Many research efforts have been directed toward models for smart city infrastructure and city service. The existing models focus on either general description for features and criteria of smart infrastructure, or domain-specific investigation in an ad-hoc way, which, for example, applies optimization to smart transportation and smart buildings. However, a general model optimally augmenting smart features into traditional infrastructure is not reported in the literature so far. We propose such a service model for smart infrastructure designers to fill this gap in two steps. A static ontology of the service model is firstly setup to identify a general framework of the underlying infrastructure expressed by a multi-agent system (MAS) and a dynamic ontology is then designed to manage informed decision-making between global and local scales inside the system. With this approach we can accomplish the design of specific smart infrastructures based on expected goals, availability of information, and the feasibility of implementation. In order to demonstrate the effectiveness of the proposed approach we design and present two smart transportation systems as examples of this procedure. We also propose some future improvements of the service model.
{"title":"An Ontology-based Service Model for Smart Infrastructure Design","authors":"Tianshu Chu, Jie Wang, J. Leckie","doi":"10.1109/CSC.2012.11","DOIUrl":"https://doi.org/10.1109/CSC.2012.11","url":null,"abstract":"As a fundamental building block of smart cities, smart infrastructure has been increasingly drawing attention in both academia and industry across the globe. Many research efforts have been directed toward models for smart city infrastructure and city service. The existing models focus on either general description for features and criteria of smart infrastructure, or domain-specific investigation in an ad-hoc way, which, for example, applies optimization to smart transportation and smart buildings. However, a general model optimally augmenting smart features into traditional infrastructure is not reported in the literature so far. We propose such a service model for smart infrastructure designers to fill this gap in two steps. A static ontology of the service model is firstly setup to identify a general framework of the underlying infrastructure expressed by a multi-agent system (MAS) and a dynamic ontology is then designed to manage informed decision-making between global and local scales inside the system. With this approach we can accomplish the design of specific smart infrastructures based on expected goals, availability of information, and the feasibility of implementation. In order to demonstrate the effectiveness of the proposed approach we design and present two smart transportation systems as examples of this procedure. We also propose some future improvements of the service model.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116599958","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}
Risto Laurikainen, Jarno Laitinen, P. Lehtovuori, J. Nurminen
Flexible allocation of resources is one of the main benefits of cloud computing. Virtualization is used to achieve this flexibility: one or more virtual machines run on a single physical machine. These virtual machines can be deployed and destroyed as needed. One obstacle to flexibility in current cloud systems is that deploying multiple virtual machines simultaneously on multiple physical machines is slow due to the inefficient usage of available resources. We implemented and evaluated three methods of transferring virtual machine images for the Open Nebula cloud middleware. One of the implementations was based on BitTorrent and the other two were based on multicast. Our evaluation results showed that the implemented methods were significantly more scalable than the default methods available in Open Nebula when tens of virtual machines were deployed simultaneously. However, the implemented methods were slower than the default unicast methods for deploying only one or a few virtual machines at a time due to overhead related to managing the transfer process. If the usage pattern of the cloud is such that deploying large batches of virtual machines at once is common, using the new transfer methods will significantly speed up the deployment process and reduce its resource usage.
{"title":"Improving the Efficiency of Deploying Virtual Machines in a Cloud Environment","authors":"Risto Laurikainen, Jarno Laitinen, P. Lehtovuori, J. Nurminen","doi":"10.1109/CSC.2012.43","DOIUrl":"https://doi.org/10.1109/CSC.2012.43","url":null,"abstract":"Flexible allocation of resources is one of the main benefits of cloud computing. Virtualization is used to achieve this flexibility: one or more virtual machines run on a single physical machine. These virtual machines can be deployed and destroyed as needed. One obstacle to flexibility in current cloud systems is that deploying multiple virtual machines simultaneously on multiple physical machines is slow due to the inefficient usage of available resources. We implemented and evaluated three methods of transferring virtual machine images for the Open Nebula cloud middleware. One of the implementations was based on BitTorrent and the other two were based on multicast. Our evaluation results showed that the implemented methods were significantly more scalable than the default methods available in Open Nebula when tens of virtual machines were deployed simultaneously. However, the implemented methods were slower than the default unicast methods for deploying only one or a few virtual machines at a time due to overhead related to managing the transfer process. If the usage pattern of the cloud is such that deploying large batches of virtual machines at once is common, using the new transfer methods will significantly speed up the deployment process and reduce its resource usage.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132300","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}
Cloud computing is a new business computing model. The environment of the resources is very complex. The resources are physically distributed and connected by the network. There are many risks existing in the resource transactions. So how to make sure that the cloud computing platform can avoid these risks during the transactions and assure the quality of services (QoS) provided to the consumers is a very important issue in cloud computing. Service Level Agreement (SLA) is proposed to solve the problems between the customers and service suppliers. Cloud Bank model [1] is a resource management model based on economic principles and aims at solving all the commercial level problems in cloud computing. This paper presents a framework of Service Level Agreement based on the Cloud Bank's liquidity risk [2] predicting model. This SLA can help the Cloud Bank avoid the risks and assure the QoS to the consumers.
{"title":"A Service Level Agreement for the Resource Transaction Risk Based on Cloud Bank Model","authors":"Mojun Su, Hao Li, Shenglin Yang, Joan Lu","doi":"10.1109/CSC.2012.38","DOIUrl":"https://doi.org/10.1109/CSC.2012.38","url":null,"abstract":"Cloud computing is a new business computing model. The environment of the resources is very complex. The resources are physically distributed and connected by the network. There are many risks existing in the resource transactions. So how to make sure that the cloud computing platform can avoid these risks during the transactions and assure the quality of services (QoS) provided to the consumers is a very important issue in cloud computing. Service Level Agreement (SLA) is proposed to solve the problems between the customers and service suppliers. Cloud Bank model [1] is a resource management model based on economic principles and aims at solving all the commercial level problems in cloud computing. This paper presents a framework of Service Level Agreement based on the Cloud Bank's liquidity risk [2] predicting model. This SLA can help the Cloud Bank avoid the risks and assure the QoS to the consumers.","PeriodicalId":183800,"journal":{"name":"2012 International Conference on Cloud and Service Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360196","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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