The proliferation of observational devices and sensors with networking capabilities has led to growth in both the rates and sources of data that ultimately contribute to extreme-scale data volumes. Datasets generated in such settings are often multidimensional, with each dimension accounting for a feature of interest. We posit that efficient evaluation of queries over such datasets must account for both the distribution of data values and the patterns in the queries themselves. Configuring query evaluation by hand is infeasible given the data volumes, dimensionality, and the rates at which new data and queries arrive. In this paper, we describe our algorithm to autonomously improve query evaluations over voluminous, distributed datasets. Our approach autonomously tunes for the most dominant query patterns and distribution of values across a dimension. We evaluate our algorithm in the context of our system, Galileo, which is a hierarchical distributed hash table used for managing geospatial, time-series data. Our system strikes a balance between memory utilization, fast evaluations, and search space reductions. Empirical evaluations reported here are performed on a dataset that is multidimensional and comprises a billion files. The schemes described in this work are broadly applicable to any system that leverages distributed hash tables as a storage mechanism.
{"title":"Autonomously improving query evaluations over multidimensional data in distributed hash tables","authors":"Matthew Malensek, S. Pallickara, S. Pallickara","doi":"10.1145/2494621.2494638","DOIUrl":"https://doi.org/10.1145/2494621.2494638","url":null,"abstract":"The proliferation of observational devices and sensors with networking capabilities has led to growth in both the rates and sources of data that ultimately contribute to extreme-scale data volumes. Datasets generated in such settings are often multidimensional, with each dimension accounting for a feature of interest. We posit that efficient evaluation of queries over such datasets must account for both the distribution of data values and the patterns in the queries themselves. Configuring query evaluation by hand is infeasible given the data volumes, dimensionality, and the rates at which new data and queries arrive. In this paper, we describe our algorithm to autonomously improve query evaluations over voluminous, distributed datasets. Our approach autonomously tunes for the most dominant query patterns and distribution of values across a dimension. We evaluate our algorithm in the context of our system, Galileo, which is a hierarchical distributed hash table used for managing geospatial, time-series data. Our system strikes a balance between memory utilization, fast evaluations, and search space reductions. Empirical evaluations reported here are performed on a dataset that is multidimensional and comprises a billion files. The schemes described in this work are broadly applicable to any system that leverages distributed hash tables as a storage mechanism.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122105549","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 storage services bring users a convenient, cross-device and practical application. A great number of users ranging from individuals to large-scale corporates grow and hold on the almighty information to cloud storage services. However, the lack of guaranteeing data security is a mortal defect resulting in the strong mistrust of users. Therefore, we propose and implement the SecureDropbox system which constructs a secure architecture including key generation, key management, file encryption, and synchronization modules to prevent risks of data disclosure.
{"title":"SecureDropbox: a file encryption system suitable for cloud storage services","authors":"Min-Yu Chen, Chi-Wei Liu, M. Hwang","doi":"10.1145/2494621.2494642","DOIUrl":"https://doi.org/10.1145/2494621.2494642","url":null,"abstract":"Cloud storage services bring users a convenient, cross-device and practical application. A great number of users ranging from individuals to large-scale corporates grow and hold on the almighty information to cloud storage services. However, the lack of guaranteeing data security is a mortal defect resulting in the strong mistrust of users. Therefore, we propose and implement the SecureDropbox system which constructs a secure architecture including key generation, key management, file encryption, and synchronization modules to prevent risks of data disclosure.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130059923","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}
According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. Especially these methods need to support effective infrastructure composed of computing resources that are used for pre-processing and post-processing of scientific data. In this paper, we describe the design of a framework to support data transformation and reduction, in which is an essential phase to handling a large scale of data in a climate simulation. In order for efficient data movement in the designed framework we use the pushpull framework provided by Apache OODT.
{"title":"Simulation process support for climate data analysis","authors":"Yunhee Kang, S. Kung, Haengjin Jang","doi":"10.1145/2494621.2494651","DOIUrl":"https://doi.org/10.1145/2494621.2494651","url":null,"abstract":"According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. Especially these methods need to support effective infrastructure composed of computing resources that are used for pre-processing and post-processing of scientific data. In this paper, we describe the design of a framework to support data transformation and reduction, in which is an essential phase to handling a large scale of data in a climate simulation. In order for efficient data movement in the designed framework we use the pushpull framework provided by Apache OODT.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116818618","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}
With the rapid growth of data centers and clouds, the power cost and power consumption of their computing and storage resources become critically important to be managed efficiently. Several research studies have shown that data servers typically operate at a low utilization of 10% to 15%, while their power consumption is close to those at peak loads. With this significant fluctuation in the workloads, an elastic delivery of computing services with an efficient power provisioning mechanism becomes an important design goal. Live workload migrations and virtualization are important techniques to optimize power and performance in large-scale data centers [5], [25] This paper presents an application specific autonomic adaptive power and performance management system that utilizes AppFlow-based reasoning to configure dynamically datacenter resources and workload allocations. This system will continuously monitor the workload to determine the current operating point of both workloads and the virtual machines (VMs) running these workloads and then predict the next operating points for these VMs. This enables the system to allocate the appropriate amount of hardware resources that can run efficiently the VM workloads with minimum power consumption. We have experimented with and evaluated our approach to manage the VMs running RUBiS bidding application. Our experimental results showed that our approach can reduce the VMs' power consumption up to 84% compared to static resource allocation and up to 30% compared to other methods with minimum performance degradation.
{"title":"Autonomic performance-per-watt management (APM) of cloud resources and services","authors":"Farah Fargo, Cihan Tunc, Y. Al-Nashif, S. Hariri","doi":"10.1145/2494621.2494624","DOIUrl":"https://doi.org/10.1145/2494621.2494624","url":null,"abstract":"With the rapid growth of data centers and clouds, the power cost and power consumption of their computing and storage resources become critically important to be managed efficiently. Several research studies have shown that data servers typically operate at a low utilization of 10% to 15%, while their power consumption is close to those at peak loads. With this significant fluctuation in the workloads, an elastic delivery of computing services with an efficient power provisioning mechanism becomes an important design goal. Live workload migrations and virtualization are important techniques to optimize power and performance in large-scale data centers [5], [25] This paper presents an application specific autonomic adaptive power and performance management system that utilizes AppFlow-based reasoning to configure dynamically datacenter resources and workload allocations. This system will continuously monitor the workload to determine the current operating point of both workloads and the virtual machines (VMs) running these workloads and then predict the next operating points for these VMs. This enables the system to allocate the appropriate amount of hardware resources that can run efficiently the VM workloads with minimum power consumption. We have experimented with and evaluated our approach to manage the VMs running RUBiS bidding application. Our experimental results showed that our approach can reduce the VMs' power consumption up to 84% compared to static resource allocation and up to 30% compared to other methods with minimum performance degradation.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622986","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}
An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.
{"title":"A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling","authors":"M. Sedaghat, F. Hernández-Rodriguez, E. Elmroth","doi":"10.1145/2494621.2494628","DOIUrl":"https://doi.org/10.1145/2494621.2494628","url":null,"abstract":"An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805090","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}
Future cloud systems will become increasingly complicated and highly heterogeneous. It is paramount to develop new techniques that can achieve high performance and low energy consumption in future cloud systems. However, this is not a trivial task because the dynamic nature of system status and user workloads requires that the system must be able to trade off performance and energy efficiency at real time. In this paper, we propose B-MAPS, a self-adaptive resource scheduling framework, which has the potential to improve the performance and energy-efficiency of multi-core or many-core based heterogeneous cloud systems.
{"title":"B-MAPS: a self-adaptive resource scheduling framework for heterogeneous cloud systems","authors":"Joal Wood, B. Romoser, I. Zecena, Ziliang Zong","doi":"10.1145/2494621.2494640","DOIUrl":"https://doi.org/10.1145/2494621.2494640","url":null,"abstract":"Future cloud systems will become increasingly complicated and highly heterogeneous. It is paramount to develop new techniques that can achieve high performance and low energy consumption in future cloud systems. However, this is not a trivial task because the dynamic nature of system status and user workloads requires that the system must be able to trade off performance and energy efficiency at real time. In this paper, we propose B-MAPS, a self-adaptive resource scheduling framework, which has the potential to improve the performance and energy-efficiency of multi-core or many-core based heterogeneous cloud systems.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133561115","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 introduces a model-based autonomic security management (ASM) approach to estimate, detect and identify security attacks along with planning a sequence of actions to effectively protect the networked computing system. In the proposed approach, sensors collect system and network parameters and send the data to the forecasters and the intrusion detection systems (IDSes). A multi-objective controller selects the optimal protection method to recover the system based on the signature of attacks. The proposed approach is demonstrated on several case studies including Denial of Service (DoS) attacks, SQL Injection attacks and memory exhaustion attacks. Experiments show that the ASM approach can successfully defend and recover the victim host from known and unknown attacks while maintaining QoS with low overheads.
{"title":"A model-based approach to self-protection in computing system","authors":"Qian Chen, S. Abdelwahed, A. Erradi","doi":"10.1145/2494621.2494639","DOIUrl":"https://doi.org/10.1145/2494621.2494639","url":null,"abstract":"This paper introduces a model-based autonomic security management (ASM) approach to estimate, detect and identify security attacks along with planning a sequence of actions to effectively protect the networked computing system. In the proposed approach, sensors collect system and network parameters and send the data to the forecasters and the intrusion detection systems (IDSes). A multi-objective controller selects the optimal protection method to recover the system based on the signature of attacks. The proposed approach is demonstrated on several case studies including Denial of Service (DoS) attacks, SQL Injection attacks and memory exhaustion attacks. Experiments show that the ASM approach can successfully defend and recover the victim host from known and unknown attacks while maintaining QoS with low overheads.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124458092","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}
Matthew Malensek, Z. Sui, Neil Harvey, S. Pallickara
Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of relevant events and conditions naturally provides a more accurate model, but also increases the computational workload associated with the simulation. To manage these processing requirements in a scalable manner, a discrete event simulation can be distributed across a number of computing resources. However, individual tasks in the simulation are stateful, and therefore require inter-task communication and synchronization to produce an accurate model. This property not only complicates the orchestration of the discrete event simulation in a distributed setting, but also makes providing reliable, fault-tolerant execution a challenge, especially when compared to conventional distributed fault tolerance schemes. In this paper, we propose an autonomous agent that provides fault tolerance functionality for discrete event simulations by predicting state changes in the simulation and adjusting its fault tolerance policy accordingly. This allows the system to avoid negatively impacting overall execution times while preserving reliability guarantees. To underscore the viability of our solution, we provide benchmarks of a production discrete event simulation that can sustain failures while running under the supervision of our fault tolerance framework.
{"title":"Autonomous, failure-resilient orchestration of distributed discrete event simulations","authors":"Matthew Malensek, Z. Sui, Neil Harvey, S. Pallickara","doi":"10.1145/2494621.2494625","DOIUrl":"https://doi.org/10.1145/2494621.2494625","url":null,"abstract":"Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of relevant events and conditions naturally provides a more accurate model, but also increases the computational workload associated with the simulation. To manage these processing requirements in a scalable manner, a discrete event simulation can be distributed across a number of computing resources. However, individual tasks in the simulation are stateful, and therefore require inter-task communication and synchronization to produce an accurate model. This property not only complicates the orchestration of the discrete event simulation in a distributed setting, but also makes providing reliable, fault-tolerant execution a challenge, especially when compared to conventional distributed fault tolerance schemes.\u0000 In this paper, we propose an autonomous agent that provides fault tolerance functionality for discrete event simulations by predicting state changes in the simulation and adjusting its fault tolerance policy accordingly. This allows the system to avoid negatively impacting overall execution times while preserving reliability guarantees. To underscore the viability of our solution, we provide benchmarks of a production discrete event simulation that can sustain failures while running under the supervision of our fault tolerance framework.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347388","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}
Hrishikesh Gadre, I. Rodero, J. Montes, M. Parashar
In recent years, MapReduce programming model and specifically its open source implementation Hadoop has been widely used by organizations to perform large-scale data processing tasks such as web-indexing, data mining as well as scientific simulations. The key benefits of this programming model include its simple programming interface and ability to process massive datasets in a scalable fashion without requiring high-end computing infrastructure. We observe that the current design of Hadoop framework assumes a centralized execution environment involving a single datacenter. This assumption leads to simplified design decisions in the Hadoop architecture regarding efficient network usage, specifically in the replica-selection policy in Hadoop Distributed File System (HDFS) and in the reduce phase scheduling algorithm. In this paper, we investigate real-world scenarios in which MapReduce programming model and specifically Hadoop framework could be used for processing large-scale, geographically scattered datasets. We show that using the Hadoop framework with default policies can cause severe performance degradation in such geographically distributed environment. We propose and evaluate extensions to Hadoop MapReduce framework to improve its performance in such environments. The evaluation demonstrates that the proposed extensions substantially outperform default policies in the Hadoop framework.
{"title":"A case for MapReduce over the internet","authors":"Hrishikesh Gadre, I. Rodero, J. Montes, M. Parashar","doi":"10.1145/2494621.2494632","DOIUrl":"https://doi.org/10.1145/2494621.2494632","url":null,"abstract":"In recent years, MapReduce programming model and specifically its open source implementation Hadoop has been widely used by organizations to perform large-scale data processing tasks such as web-indexing, data mining as well as scientific simulations. The key benefits of this programming model include its simple programming interface and ability to process massive datasets in a scalable fashion without requiring high-end computing infrastructure. We observe that the current design of Hadoop framework assumes a centralized execution environment involving a single datacenter. This assumption leads to simplified design decisions in the Hadoop architecture regarding efficient network usage, specifically in the replica-selection policy in Hadoop Distributed File System (HDFS) and in the reduce phase scheduling algorithm. In this paper, we investigate real-world scenarios in which MapReduce programming model and specifically Hadoop framework could be used for processing large-scale, geographically scattered datasets. We show that using the Hadoop framework with default policies can cause severe performance degradation in such geographically distributed environment. We propose and evaluate extensions to Hadoop MapReduce framework to improve its performance in such environments. The evaluation demonstrates that the proposed extensions substantially outperform default policies in the Hadoop framework.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116146990","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}
Wenjin Hu, Andrew Hicks, Long Zhang, Eli M. Dow, Vinay Soni, Hao Jiang, Ronny L. Bull, Jeanna Neefe Matthews
Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.
{"title":"A quantitative study of virtual machine live migration","authors":"Wenjin Hu, Andrew Hicks, Long Zhang, Eli M. Dow, Vinay Soni, Hao Jiang, Ronny L. Bull, Jeanna Neefe Matthews","doi":"10.1145/2494621.2494622","DOIUrl":"https://doi.org/10.1145/2494621.2494622","url":null,"abstract":"Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779843","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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