Kwonyong Lee, Yoonsung Nam, Taekhee Kim, Sungyong Park
With the proliferation of cloud computing and virtual machine technologies, MapReduce applications are increasingly deployed in clouds to leverage the full potential of cloud computing environments. However, the MapReduce, which is generally used for processing large amount of data, suffers from the I/O virtualization overheads and resource competitions among virtual machines when it is run on virtual clouds. This paper proposes an adaptive data transfer algorithm in virtual MapReduce clusters. The proposed algorithm utilizes a block device reconfiguration scheme, where a block device attached to a virtual machine can be dynamically detached and reattached to other virtual machines hosted in the same physical machine. By reconfiguring the block devices, we can easily move files across different virtual machines located at the same physical machine without any network transfers between virtual machines. When the output of each map task is transferred to the reducer, this algorithm adaptively determines an appropriate transfer method between network transfer and block device reconfiguration based on current CPU utilization values and the data size for the transfer. Even in the case of data transfer between virtual machines across multiple physical machines, we can remove the transfer overheads between the virtual machine and the driver domain, which results in reducing the data transfer time and performance effects to other virtual machines in the shuffle phase. We have implemented our algorithm in Hadoop MapReduce. The benchmarking results show that the overheads incurred by transferring data from mapper virtual machines to reducer virtual machines are minimized and the execution times of MapReduce applications are shortened.
{"title":"An adaptive data transfer algorithm using block device reconfiguration in virtual MapReduce clusters","authors":"Kwonyong Lee, Yoonsung Nam, Taekhee Kim, Sungyong Park","doi":"10.1145/2494621.2494645","DOIUrl":"https://doi.org/10.1145/2494621.2494645","url":null,"abstract":"With the proliferation of cloud computing and virtual machine technologies, MapReduce applications are increasingly deployed in clouds to leverage the full potential of cloud computing environments. However, the MapReduce, which is generally used for processing large amount of data, suffers from the I/O virtualization overheads and resource competitions among virtual machines when it is run on virtual clouds. This paper proposes an adaptive data transfer algorithm in virtual MapReduce clusters. The proposed algorithm utilizes a block device reconfiguration scheme, where a block device attached to a virtual machine can be dynamically detached and reattached to other virtual machines hosted in the same physical machine. By reconfiguring the block devices, we can easily move files across different virtual machines located at the same physical machine without any network transfers between virtual machines. When the output of each map task is transferred to the reducer, this algorithm adaptively determines an appropriate transfer method between network transfer and block device reconfiguration based on current CPU utilization values and the data size for the transfer. Even in the case of data transfer between virtual machines across multiple physical machines, we can remove the transfer overheads between the virtual machine and the driver domain, which results in reducing the data transfer time and performance effects to other virtual machines in the shuffle phase. We have implemented our algorithm in Hadoop MapReduce. The benchmarking results show that the overheads incurred by transferring data from mapper virtual machines to reducer virtual machines are minimized and the execution times of MapReduce applications are shortened.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"72 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":"125294086","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 advance of cloud computing technologies, there is a huge demand for computing resources and storage. Many organizations prefer to outsource their storage and other resources. As the data reside on the third parties data centers, security is becoming a major concern. In this paper we propose a Resilient Cloud Storage (RCS) architecture that addresses the major security issues for cloud storage such as access control confidentiality, integrity, and secure communications. Our resilient approach is based on moving target defense and key hopping techniques. Data is partitioned into a random number of partitions where different keys are used to encrypt each partition. We also show that by using key hopping technique, we can reduce smaller key length that is normally used to improve performance without compromising the security. Our experimental results show that we can improve performance by 50% when we use a key of length 512 when compared with certificate technique that uses key length of 2048.
{"title":"Resilient cloud data storage services","authors":"Hemayamini Kurra, Y. Al-Nashif, S. Hariri","doi":"10.1145/2494621.2494634","DOIUrl":"https://doi.org/10.1145/2494621.2494634","url":null,"abstract":"With the advance of cloud computing technologies, there is a huge demand for computing resources and storage. Many organizations prefer to outsource their storage and other resources. As the data reside on the third parties data centers, security is becoming a major concern. In this paper we propose a Resilient Cloud Storage (RCS) architecture that addresses the major security issues for cloud storage such as access control confidentiality, integrity, and secure communications. Our resilient approach is based on moving target defense and key hopping techniques. Data is partitioned into a random number of partitions where different keys are used to encrypt each partition. We also show that by using key hopping technique, we can reduce smaller key length that is normally used to improve performance without compromising the security. Our experimental results show that we can improve performance by 50% when we use a key of length 512 when compared with certificate technique that uses key length of 2048.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"10 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":"115019509","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 this paper, we present a scalable and practical problem diagnosis framework for Hadoop environments. Our design features a decentralized approach based on hierarchical grouping and a novel non-parametric diagnostic mechanism. We evaluate our framework under various Hadoop workloads. The experimental results show that our design outperforms traditional methods significantly in the context of complex anomaly patterns and high anomaly probability.
{"title":"A scalable, non-parametric anomaly detection framework for Hadoop","authors":"Li Yu, Z. Lan","doi":"10.1145/2494621.2494643","DOIUrl":"https://doi.org/10.1145/2494621.2494643","url":null,"abstract":"In this paper, we present a scalable and practical problem diagnosis framework for Hadoop environments. Our design features a decentralized approach based on hierarchical grouping and a novel non-parametric diagnostic mechanism. We evaluate our framework under various Hadoop workloads. The experimental results show that our design outperforms traditional methods significantly in the context of complex anomaly patterns and high anomaly probability.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"29 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":"114678826","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}
Autonomic management of clouds has received a lot of attention by both academia and industry putting a lot of efforts into investigation of various solutions, even though the focus has been mainly on the IaaS level, while the PaaS level being less often addressed. However, with ever-expanding software environments of cloud application platforms, the self-management at the PaaS level becomes a major concern. We claim that run-time monitoring and detection of critical situations is a fundamental requirement to achieve autonomic behaviour in service-based cloud platforms. Accordingly, we present our novel vision of cloud application platforms as sensor networks -- computer accessible networks of distributed devices using sensors to monitor conditions at different locations. The vision is based on the similarities between the problem domain of cloud application platform monitoring and such sensor-enabled domains as traffic surveillance, environmental monitoring or home automation. We also discuss potential benefits and shortcomings associated with the presented concepts and ideas.
{"title":"A vision for monitoring cloud application platforms as sensor networks","authors":"R. Dautov, I. Paraskakis","doi":"10.1145/2494621.2494647","DOIUrl":"https://doi.org/10.1145/2494621.2494647","url":null,"abstract":"Autonomic management of clouds has received a lot of attention by both academia and industry putting a lot of efforts into investigation of various solutions, even though the focus has been mainly on the IaaS level, while the PaaS level being less often addressed. However, with ever-expanding software environments of cloud application platforms, the self-management at the PaaS level becomes a major concern. We claim that run-time monitoring and detection of critical situations is a fundamental requirement to achieve autonomic behaviour in service-based cloud platforms. Accordingly, we present our novel vision of cloud application platforms as sensor networks -- computer accessible networks of distributed devices using sensors to monitor conditions at different locations. The vision is based on the similarities between the problem domain of cloud application platform monitoring and such sensor-enabled domains as traffic surveillance, environmental monitoring or home automation. We also discuss potential benefits and shortcomings associated with the presented concepts and ideas.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"66 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":"125934208","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}
Peer-to-Peer (P2P) desktop grid computing systems circumvent the performance bottleneck and limited scalability of centralized Grid architectures resulting in a massively scalable and robust system. We have designed a set of protocols that implement a distributed, decentralized desktop grid via P2P techniques. Incoming jobs having different types of resource requirements are matched with system nodes through proximity in an N-dimensional resource space. In this paper, we address problems that arise from static load balancing mechanisms for assigning jobs to nodes that can arise for various reasons, including the heterogeneity of the available nodes or the jobs to be run, and from stale information in the P2P system. We greatly improve upon our prior work by providing lightweight yet effective dynamic load balancing mechanisms to overcome load imbalances caused by the limitations of the initial static job assignment scheme. Unlike other systems, we can effectively support resource constraints of jobs during the course of redistribution since we simplify the problem of matchmaking through building a multi-dimensional resource space and mapping jobs and nodes to this space. Throughout extensive simulation results, we show that dynamic load balancing makes the overall system more scalable, by improving system throughput and response time with low additional overhead.
{"title":"Autonomic load balancing mechanisms in the P2P desktop grid","authors":"Jik-Soo Kim, Beomseok Nam, A. Sussman","doi":"10.1145/2494621.2494646","DOIUrl":"https://doi.org/10.1145/2494621.2494646","url":null,"abstract":"Peer-to-Peer (P2P) desktop grid computing systems circumvent the performance bottleneck and limited scalability of centralized Grid architectures resulting in a massively scalable and robust system. We have designed a set of protocols that implement a distributed, decentralized desktop grid via P2P techniques. Incoming jobs having different types of resource requirements are matched with system nodes through proximity in an N-dimensional resource space.\u0000 In this paper, we address problems that arise from static load balancing mechanisms for assigning jobs to nodes that can arise for various reasons, including the heterogeneity of the available nodes or the jobs to be run, and from stale information in the P2P system. We greatly improve upon our prior work by providing lightweight yet effective dynamic load balancing mechanisms to overcome load imbalances caused by the limitations of the initial static job assignment scheme. Unlike other systems, we can effectively support resource constraints of jobs during the course of redistribution since we simplify the problem of matchmaking through building a multi-dimensional resource space and mapping jobs and nodes to this space. Throughout extensive simulation results, we show that dynamic load balancing makes the overall system more scalable, by improving system throughput and response time with low additional overhead.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"55 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":"115655830","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}
Content dissemination systems comprise a large number of nodes that organize themselves into different topologies. In this paper, we explore the role of topologies in autonomously coping with failures. The topologies we consider are based on regular, random, small-world, and power law graphs. Connections within these topologies can account for network proximity and are suitable for real-time communications. We explore specific attributes of a topology that contribute to its failure resiliency. The metrics that we use to profile this resilience include: communication path lengths, network partitions, migration of workloads, and the impact on system throughput. This research will allow designers to choose topologies or configure metrics for a specific topology to achieve their performance objectives.
{"title":"On the role of topology in autonomously coping with failures in content dissemination systems","authors":"Ryan Stern, S. Pallickara","doi":"10.1145/2494621.2494635","DOIUrl":"https://doi.org/10.1145/2494621.2494635","url":null,"abstract":"Content dissemination systems comprise a large number of nodes that organize themselves into different topologies. In this paper, we explore the role of topologies in autonomously coping with failures. The topologies we consider are based on regular, random, small-world, and power law graphs. Connections within these topologies can account for network proximity and are suitable for real-time communications. We explore specific attributes of a topology that contribute to its failure resiliency. The metrics that we use to profile this resilience include: communication path lengths, network partitions, migration of workloads, and the impact on system throughput. This research will allow designers to choose topologies or configure metrics for a specific topology to achieve their performance objectives.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"95 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":"120874073","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}
Lena Mashayekhy, Mahyar Movahed Nejad, Daniel Grosu
One of the major challenges faced by the cloud providers is to allocate and provision the resources such that their profit is maximized and the resources are utilized efficiently. We address this challenge by designing an autonomic VM (Virtual Machine) provisioning and allocation mechanism that adapts to the changing user demands. We show that the proposed mechanism is a PTAS (Polynomial-Time Approximation Scheme) and that it is truthful, that is, the users do not have incentives to lie about their requested bundles of VM instances and their valuations. We perform extensive experiments in order to investigate the properties of the mechanism.
{"title":"A truthful approximation mechanism for autonomic virtual machine provisioning and allocation in clouds","authors":"Lena Mashayekhy, Mahyar Movahed Nejad, Daniel Grosu","doi":"10.1145/2494621.2494637","DOIUrl":"https://doi.org/10.1145/2494621.2494637","url":null,"abstract":"One of the major challenges faced by the cloud providers is to allocate and provision the resources such that their profit is maximized and the resources are utilized efficiently. We address this challenge by designing an autonomic VM (Virtual Machine) provisioning and allocation mechanism that adapts to the changing user demands. We show that the proposed mechanism is a PTAS (Polynomial-Time Approximation Scheme) and that it is truthful, that is, the users do not have incentives to lie about their requested bundles of VM instances and their valuations. We perform extensive experiments in order to investigate the properties of the mechanism.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"22 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":"121268887","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}
While public cloud computing platforms have become popular in recent years, private clouds---operated by enterprises for their internal use---have also begun gaining traction. The configuration and continuous tuning of a private cloud to meet user demands is a complex task. While private cloud management frameworks provide a number of flexible configuration options for this purpose, they leave it to the administrator to determine how to best configure and tune the cloud platform for local needs. In this paper, we argue for an adaptive control plane for private clouds that simplifies the tasks of configuring and operating a private cloud such that each control plane service is adaptive to the workload seen due to end-user requests. We present a logistic regression model to automate the provisioning and dynamic reconfiguration of control plane services in a private cloud. We implement our approach for two control plane services---monitoring and messaging---for OpenStack-based private clouds. Our experimental results on a laboratory private cloud testbed and using public cloud workloads demonstrates the ability of our approach to provision and adapt such services from very small to very large private cloud configurations.
{"title":"A flexible elastic control plane for private clouds","authors":"Upendra Sharma, P. Shenoy, S. Sahu","doi":"10.1145/2494621.2494626","DOIUrl":"https://doi.org/10.1145/2494621.2494626","url":null,"abstract":"While public cloud computing platforms have become popular in recent years, private clouds---operated by enterprises for their internal use---have also begun gaining traction. The configuration and continuous tuning of a private cloud to meet user demands is a complex task. While private cloud management frameworks provide a number of flexible configuration options for this purpose, they leave it to the administrator to determine how to best configure and tune the cloud platform for local needs. In this paper, we argue for an adaptive control plane for private clouds that simplifies the tasks of configuring and operating a private cloud such that each control plane service is adaptive to the workload seen due to end-user requests. We present a logistic regression model to automate the provisioning and dynamic reconfiguration of control plane services in a private cloud. We implement our approach for two control plane services---monitoring and messaging---for OpenStack-based private clouds. Our experimental results on a laboratory private cloud testbed and using public cloud workloads demonstrates the ability of our approach to provision and adapt such services from very small to very large private cloud configurations.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"23 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":"125123189","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}
Jinha Kim, S. Elnikety, Yuxiong He, Seung-won Hwang, Shaolei Ren
Web servers provide content to users, with the requirement of providing high response quality within a short response time. Meeting these requirements is challenging, especially in the event of load spikes. Meanwhile, we observe that a response to a request can be adapted or partially executed depending on current resource availability at the server. For example, a web server can choose to send a low or medium resolution image instead of sending the original high resolution image under resource contention. In this paper, we exploit partial execution to expose a trade off between resource consumption and service quality. We show how to manage server resources to improve service quality and responsiveness. Specifically, we develop a framework, called Quota-based Control Optimization (QACO). The quota represents the total amount of resources available for all pending requests. QACO consists of two modules: (1) A control module adjusts the quota to meet the response time target. (2) An optimization module exploits partial execution and allocates the quota to pending requests in a manner that improves total response quality. We evaluate the framework using a system implementation in the Apache Web server, and using a simulation study of a Video-on-Demand server. The results show that under a response time target, QACO achieves a higher response quality than traditional techniques that admit or reject requests without exploiting partial execution.
{"title":"QACO: exploiting partial execution in web servers","authors":"Jinha Kim, S. Elnikety, Yuxiong He, Seung-won Hwang, Shaolei Ren","doi":"10.1145/2494621.2494636","DOIUrl":"https://doi.org/10.1145/2494621.2494636","url":null,"abstract":"Web servers provide content to users, with the requirement of providing high response quality within a short response time. Meeting these requirements is challenging, especially in the event of load spikes. Meanwhile, we observe that a response to a request can be adapted or partially executed depending on current resource availability at the server. For example, a web server can choose to send a low or medium resolution image instead of sending the original high resolution image under resource contention.\u0000 In this paper, we exploit partial execution to expose a trade off between resource consumption and service quality. We show how to manage server resources to improve service quality and responsiveness. Specifically, we develop a framework, called Quota-based Control Optimization (QACO). The quota represents the total amount of resources available for all pending requests. QACO consists of two modules: (1) A control module adjusts the quota to meet the response time target. (2) An optimization module exploits partial execution and allocates the quota to pending requests in a manner that improves total response quality. We evaluate the framework using a system implementation in the Apache Web server, and using a simulation study of a Video-on-Demand server. The results show that under a response time target, QACO achieves a higher response quality than traditional techniques that admit or reject requests without exploiting partial execution.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"40 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":"117189317","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}
Virtualization technology reduces the costs for server installation, operation, and maintenance and it can simplify the building of distributed systems. Currently, there are various types of virtualization technologies such as Xen, KVM, VMware, etc, and these technologies support various virtualization functions individually on heterogeneous platforms. Therefore, it is required to integrate and manage these heterogeneous virtualized resources in order to build a distributed system supporting current virtualization techniques. In this paper, we propose an integrated management system for managing heterogenous virtual resources. The proposed system is developed based on the following two techniques: a libvirt-based virtualization API and Data Distribution Service (DDS). Libvirt-based virtualization API is to extract information of heterogeneous virtual resources and to control them. DDS is to transmit in real-time the state information and control commands of virtualized resources.
{"title":"An integrated management system of virtual resources based on virtualization API and data distribution service","authors":"Yongseong Cho, Jongsun Choi, Jaeyoung Choi","doi":"10.1145/2494621.2494648","DOIUrl":"https://doi.org/10.1145/2494621.2494648","url":null,"abstract":"Virtualization technology reduces the costs for server installation, operation, and maintenance and it can simplify the building of distributed systems. Currently, there are various types of virtualization technologies such as Xen, KVM, VMware, etc, and these technologies support various virtualization functions individually on heterogeneous platforms. Therefore, it is required to integrate and manage these heterogeneous virtualized resources in order to build a distributed system supporting current virtualization techniques. In this paper, we propose an integrated management system for managing heterogenous virtual resources. The proposed system is developed based on the following two techniques: a libvirt-based virtualization API and Data Distribution Service (DDS). Libvirt-based virtualization API is to extract information of heterogeneous virtual resources and to control them. DDS is to transmit in real-time the state information and control commands of virtualized resources.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"15 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":"130228427","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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