This paper introduces some of the example applications handling extremely big data with supercomputers such as large-scale network analysis, X10-based large-scale graph analytics library, Graph500 benchmark, and billion-scale social simulation.
{"title":"Extreme big data processing in large-scale graph analytics and billion-scale social simulation","authors":"T. Suzumura","doi":"10.1145/2568088.2576096","DOIUrl":"https://doi.org/10.1145/2568088.2576096","url":null,"abstract":"This paper introduces some of the example applications handling extremely big data with supercomputers such as large-scale network analysis, X10-based large-scale graph analytics library, Graph500 benchmark, and billion-scale social simulation.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131555936","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}
D. Redlich, Ulrich Winkler, T. Molka, Wasif Gilani
Modern businesses and business processes depend on an increasingly interconnected set of resources, which can be affected by external and internal factors at any time. Threats like natural disasters, terrorism, or even power blackouts potentially cause disruptions in an organisation's resource infrastructure which in turn negatively impacts the performance of dependent business processes. In order to assist business analysts dealing with this ever increasing complexity of interdependent business structures a model-driven workbench named Model-Driven Business Impact Analysis (MDBIA) has been developed with the purpose of predicting consequences on the business process level for an organisation in case of disruptions. An already existing Model-Driven Performance Engineering (MDPE) workbench, which originally provided process-centric performance decision support, has been adapted and extended to meet the additional requirements of business impact analysis. The fundamental concepts of the resulting MDBIA workbench, which include the introduction of the applied key models and transformation chain, are presented and evaluated in this paper.
{"title":"Model-driven engineering in practice: integrated performance decision support for process-centric business impact analysis","authors":"D. Redlich, Ulrich Winkler, T. Molka, Wasif Gilani","doi":"10.1145/2568088.2576797","DOIUrl":"https://doi.org/10.1145/2568088.2576797","url":null,"abstract":"Modern businesses and business processes depend on an increasingly interconnected set of resources, which can be affected by external and internal factors at any time. Threats like natural disasters, terrorism, or even power blackouts potentially cause disruptions in an organisation's resource infrastructure which in turn negatively impacts the performance of dependent business processes. In order to assist business analysts dealing with this ever increasing complexity of interdependent business structures a model-driven workbench named Model-Driven Business Impact Analysis (MDBIA) has been developed with the purpose of predicting consequences on the business process level for an organisation in case of disruptions. An already existing Model-Driven Performance Engineering (MDPE) workbench, which originally provided process-centric performance decision support, has been adapted and extended to meet the additional requirements of business impact analysis. The fundamental concepts of the resulting MDBIA workbench, which include the introduction of the applied key models and transformation chain, are presented and evaluated in this paper.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127136254","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}
JAQL is a query language for large-scale data that connects BigData analytics and MapReduce framework together. Also an IBM product, JAQL's performance is critical for IBM InfoSphere BigInsights, a BigData analytics platform. In this paper, we report our work on improving JAQL performance from multiple perspectives. We explore the parallelism of JAQL, profile JAQL for performance analysis, identify I/O as the dominant performance bottleneck, and improve JAQL performance with an emphasis on reducing I/O data size and increasing (de)serialization efficiency. With TPCH benchmark on a simple Hadoop cluster, we report up to 2x performance improvements in JAQL with our optimization fixes.
{"title":"Run-time performance optimization of a BigData query language","authors":"Yanbin Liu, Parijat Dube, Scott Gray","doi":"10.1145/2568088.2576800","DOIUrl":"https://doi.org/10.1145/2568088.2576800","url":null,"abstract":"JAQL is a query language for large-scale data that connects BigData analytics and MapReduce framework together. Also an IBM product, JAQL's performance is critical for IBM InfoSphere BigInsights, a BigData analytics platform. In this paper, we report our work on improving JAQL performance from multiple perspectives. We explore the parallelism of JAQL, profile JAQL for performance analysis, identify I/O as the dominant performance bottleneck, and improve JAQL performance with an emphasis on reducing I/O data size and increasing (de)serialization efficiency. With TPCH benchmark on a simple Hadoop cluster, we report up to 2x performance improvements in JAQL with our optimization fixes.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128388677","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}
In the past decade, the IT industry has experienced a paradigm shift as computing resources became available as a utility through cloud based services. In spite of the wider adoption of cloud computing platforms, some businesses and organizations hesitate to move all their applications to the cloud due to performance concerns. Existing practices in application performance management rely heavily on white-box modeling and diagnosis approaches or on performance troubleshooting "cookbooks" to find potential bottlenecks and remediation steps. However, the scalability and adaptivity of such approaches remain severely constrained, especially in a highly-dynamic, consolidated cloud environment. For performance isolation and differentiation, most modern hypervisors offer powerful resource control primitives such as reservations, limits, and shares for individual virtual machines (VMs). Even so, with the exploding growth of virtual machine sprawl, setting these controls properly such that co-located virtualized applications get enough resources to meet their respective service level objectives (SLOs) becomes a nearly insoluble task. These challenges present unique opportunities in leveraging the rich telemetry collected from applications and systems in the cloud, and in applying statistical learning, optimization, and control based techniques to developing model-based, automated application performance management frameworks. There has been a large body of research in this area in the last several years, but many problems remain. In this talk, I'll highlight some of the automated and data-driven performance management techniques we have developed, along with related technical challenges. I'll then discuss open research problems, in hope to attract more innovative ideas and solutions from a larger community of researchers and practitioners.
{"title":"Application performance management using learning, optimization, and control","authors":"Xiaoyun Zhu","doi":"10.1145/2568088.2576098","DOIUrl":"https://doi.org/10.1145/2568088.2576098","url":null,"abstract":"In the past decade, the IT industry has experienced a paradigm shift as computing resources became available as a utility through cloud based services. In spite of the wider adoption of cloud computing platforms, some businesses and organizations hesitate to move all their applications to the cloud due to performance concerns. Existing practices in application performance management rely heavily on white-box modeling and diagnosis approaches or on performance troubleshooting \"cookbooks\" to find potential bottlenecks and remediation steps. However, the scalability and adaptivity of such approaches remain severely constrained, especially in a highly-dynamic, consolidated cloud environment. For performance isolation and differentiation, most modern hypervisors offer powerful resource control primitives such as reservations, limits, and shares for individual virtual machines (VMs). Even so, with the exploding growth of virtual machine sprawl, setting these controls properly such that co-located virtualized applications get enough resources to meet their respective service level objectives (SLOs) becomes a nearly insoluble task. These challenges present unique opportunities in leveraging the rich telemetry collected from applications and systems in the cloud, and in applying statistical learning, optimization, and control based techniques to developing model-based, automated application performance management frameworks. There has been a large body of research in this area in the last several years, but many problems remain. In this talk, I'll highlight some of the automated and data-driven performance management techniques we have developed, along with related technical challenges. I'll then discuss open research problems, in hope to attract more innovative ideas and solutions from a larger community of researchers and practitioners.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115599254","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}
Mark D. Syer, Z. Jiang, M. Nagappan, A. Hassan, Mohamed N. Nasser, P. Flora
Ultra-Large-Scale (ULS) systems face continuously evolving field workloads in terms of activated/disabled feature sets, varying usage patterns and changing deployment configurations. These evolving workloads often have a large impact, on the performance of a ULS system. Hence, continuous load testing is critical to ensuring the error-free operation of such systems. A common challenge facing performance analysts is to validate if a load test closely resembles the current field workloads. Such validation may be performed by comparing execution logs from the load test and the field. However, the size and unstructured nature of execution logs makes such a comparison unfeasible without automated support. In this paper, we propose an automated approach to validate whether a load test resembles the field workload and, if not, determines how they differ by compare execution logs from a load test and the field. Performance analysts can then update their load test cases to eliminate such differences, hence creating more realistic load test cases. We perform three case studies on two large systems: one open-source system and one enterprise system. Our approach identifies differences between load tests and the field with a precision of >75% compared to only >16% for the state-of-the-practice.
{"title":"Continuous validation of load test suites","authors":"Mark D. Syer, Z. Jiang, M. Nagappan, A. Hassan, Mohamed N. Nasser, P. Flora","doi":"10.1145/2568088.2568101","DOIUrl":"https://doi.org/10.1145/2568088.2568101","url":null,"abstract":"Ultra-Large-Scale (ULS) systems face continuously evolving field workloads in terms of activated/disabled feature sets, varying usage patterns and changing deployment configurations. These evolving workloads often have a large impact, on the performance of a ULS system. Hence, continuous load testing is critical to ensuring the error-free operation of such systems. A common challenge facing performance analysts is to validate if a load test closely resembles the current field workloads. Such validation may be performed by comparing execution logs from the load test and the field. However, the size and unstructured nature of execution logs makes such a comparison unfeasible without automated support. In this paper, we propose an automated approach to validate whether a load test resembles the field workload and, if not, determines how they differ by compare execution logs from a load test and the field. Performance analysts can then update their load test cases to eliminate such differences, hence creating more realistic load test cases. We perform three case studies on two large systems: one open-source system and one enterprise system. Our approach identifies differences between load tests and the field with a precision of >75% compared to only >16% for the state-of-the-practice.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127587218","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}
The performance analysis of a server application and the sizing of the hardware required to host it in a data center continue to be pressing issues today. With most server-grade computers now built with "frequency-scaled CPUs" and other such devices, it has become important to answer performance and sizing questions in the presence of such hardware. PowerPerfCenter is an application performance modeling tool that allows specification of devices whose operating speeds can change dynamically. It also estimates power usage by the machines in presence of such devices. Furthermore, it allows specification of a dynamic workload which is required to understand the impact of power management. We validated the performance metrics predicted by PowerPerfCenter against measured ones of an application deployed on a test-bed consisting of frequency-scaled CPUs, and found the match to be good. We also used PowerPerfCenter to show that power savings may not be significant if a device does not have different idle power consumption when configured with different operating speeds.
{"title":"PowerPerfCenter: a power and performance prediction tool for multi-tier applications","authors":"V. Apte, Bhavin J. Doshi","doi":"10.1145/2568088.2576758","DOIUrl":"https://doi.org/10.1145/2568088.2576758","url":null,"abstract":"The performance analysis of a server application and the sizing of the hardware required to host it in a data center continue to be pressing issues today. With most server-grade computers now built with \"frequency-scaled CPUs\" and other such devices, it has become important to answer performance and sizing questions in the presence of such hardware. PowerPerfCenter is an application performance modeling tool that allows specification of devices whose operating speeds can change dynamically. It also estimates power usage by the machines in presence of such devices. Furthermore, it allows specification of a dynamic workload which is required to understand the impact of power management. We validated the performance metrics predicted by PowerPerfCenter against measured ones of an application deployed on a test-bed consisting of frequency-scaled CPUs, and found the match to be good. We also used PowerPerfCenter to show that power savings may not be significant if a device does not have different idle power consumption when configured with different operating speeds.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129844234","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}
Tomás Martinec, L. Marek, A. Steinhauser, P. Tůma, Qais Noorshams, A. Rentschler, Ralf H. Reussner
Middleware performance models are useful building blocks in the performance models of distributed software applications. We focus on performance models of messaging middleware implementing the Java Message Service standard, showing how certain system design properties -- including pipelined processing and message coalescing -- interact to create performance behavior that the existing models do not capture accurately. We construct a performance model of the ActiveMQ messaging middleware that addresses the outlined issues and discuss how the approach extends to other middleware implementations.
{"title":"Constructing performance model of JMS middleware platform","authors":"Tomás Martinec, L. Marek, A. Steinhauser, P. Tůma, Qais Noorshams, A. Rentschler, Ralf H. Reussner","doi":"10.1145/2568088.2568096","DOIUrl":"https://doi.org/10.1145/2568088.2568096","url":null,"abstract":"Middleware performance models are useful building blocks in the performance models of distributed software applications. We focus on performance models of messaging middleware implementing the Java Message Service standard, showing how certain system design properties -- including pipelined processing and message coalescing -- interact to create performance behavior that the existing models do not capture accurately. We construct a performance model of the ActiveMQ messaging middleware that addresses the outlined issues and discuss how the approach extends to other middleware implementations.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123429726","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}
Simon Spinner, G. Casale, Xiaoyun Zhu, Samuel Kounev
When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.
{"title":"LibReDE: a library for resource demand estimation","authors":"Simon Spinner, G. Casale, Xiaoyun Zhu, Samuel Kounev","doi":"10.1145/2568088.2576093","DOIUrl":"https://doi.org/10.1145/2568088.2576093","url":null,"abstract":"When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324742","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 the need for scaled-out systems increases, it is paramount to architect them as large distributed systems consisting of off-the-shelf basic computing components known as compute or data nodes. These nodes are expected to handle their work independently, and often utilize off-the-shelf management tools, like those offered by Linux, to differentiate priorities of tasks. While prioritization of background tasks in server nodes takes center stage in scaled-out systems, with many tasks associated with salient features such as eventual consistency, data analytics, and garbage collection, the standard Linux tools such as nice and ionice fail to adapt to the dynamic behavior of high priority tasks in order to achieve the best trade-off between protecting the performance of high priority workload and completing as much low priority work as possible. In this paper, we provide a solution by proposing a priority scheduling middleware that employs different policies to schedule background tasks based on the instantaneous resource requirements of the high priority applications running on the server node. The selection of policies is based on off-line and on-line learning of the high priority workload characteristics and the imposed performance impact due to low priority work. In effect, this middleware uses a {em hybrid} approach to scheduling rather than a monolithic policy. We prototype and evaluate it via measurements on a test-bed and show that this scheduling middleware is robust as it effectively and autonomically changes the relative priorities between high and low priority tasks, consistently meeting their competing performance targets.
{"title":"Agile middleware for scheduling: meeting competing performance requirements of diverse tasks","authors":"Feng Yan, S. Hughes, Alma Riska, E. Smirni","doi":"10.1145/2568088.2568104","DOIUrl":"https://doi.org/10.1145/2568088.2568104","url":null,"abstract":"As the need for scaled-out systems increases, it is paramount to architect them as large distributed systems consisting of off-the-shelf basic computing components known as compute or data nodes. These nodes are expected to handle their work independently, and often utilize off-the-shelf management tools, like those offered by Linux, to differentiate priorities of tasks. While prioritization of background tasks in server nodes takes center stage in scaled-out systems, with many tasks associated with salient features such as eventual consistency, data analytics, and garbage collection, the standard Linux tools such as nice and ionice fail to adapt to the dynamic behavior of high priority tasks in order to achieve the best trade-off between protecting the performance of high priority workload and completing as much low priority work as possible. In this paper, we provide a solution by proposing a priority scheduling middleware that employs different policies to schedule background tasks based on the instantaneous resource requirements of the high priority applications running on the server node. The selection of policies is based on off-line and on-line learning of the high priority workload characteristics and the imposed performance impact due to low priority work. In effect, this middleware uses a {em hybrid} approach to scheduling rather than a monolithic policy. We prototype and evaluate it via measurements on a test-bed and show that this scheduling middleware is robust as it effectively and autonomically changes the relative priorities between high and low priority tasks, consistently meeting their competing performance targets.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128897771","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}
This paper focuses on devising efficient resource management techniques used by the resource management middleware in clouds that handle MapReduce jobs with end-to-end service level agreements (SLAs) comprising an earliest start time, execution time, and a deadline. This research and development work, performed in collaboration with our industrial partner, presents the formulation of the matchmaking and scheduling problem for MapReduce jobs as an optimization problem using: Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques. In addition to the formulations devised, our experience in implementing the MILP and CP models using various open source as well as commercial software packages is described. Furthermore, a performance evaluation of the different approaches used to implement the formulations is conducted using a variety of different workloads.
{"title":"Engineering resource management middleware for optimizing the performance of clouds processing mapreduce jobs with deadlines","authors":"Norman Lim, S. Majumdar, P. Ashwood-Smith","doi":"10.1145/2568088.2576796","DOIUrl":"https://doi.org/10.1145/2568088.2576796","url":null,"abstract":"This paper focuses on devising efficient resource management techniques used by the resource management middleware in clouds that handle MapReduce jobs with end-to-end service level agreements (SLAs) comprising an earliest start time, execution time, and a deadline. This research and development work, performed in collaboration with our industrial partner, presents the formulation of the matchmaking and scheduling problem for MapReduce jobs as an optimization problem using: Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques. In addition to the formulations devised, our experience in implementing the MILP and CP models using various open source as well as commercial software packages is described. Furthermore, a performance evaluation of the different approaches used to implement the formulations is conducted using a variety of different workloads.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129003014","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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