Zhou Fang, Mulong Luo, Tong Yu, O. Mengshoel, M. Srivastava, Rajesh K. Gupta
This work considers that multiple mobile clients offload various continuous sensing applications with end-to-end delay constraints, to a cluster of machines as the server. Contention for shared computing resources on a server can result in delay degradation and application malfunction. We present ATOMS (Accurate Timing prediction and Offloading for Mobile Systems), a framework to mitigate multi-tenant resource contention and to improve delay using a two-phase Plan-Schedule approach. The planning phase includes methods to predict future workloads from all clients, to estimate contention, and to devise offloading schedule to reduce contention. The scheduling phase dispatches arriving offloaded workload to the server machine that minimizes contention, based on the running workloads on each machine.
{"title":"Mitigating multi-tenant interference on mobile offloading servers: poster abstract","authors":"Zhou Fang, Mulong Luo, Tong Yu, O. Mengshoel, M. Srivastava, Rajesh K. Gupta","doi":"10.1145/3127479.3132563","DOIUrl":"https://doi.org/10.1145/3127479.3132563","url":null,"abstract":"This work considers that multiple mobile clients offload various continuous sensing applications with end-to-end delay constraints, to a cluster of machines as the server. Contention for shared computing resources on a server can result in delay degradation and application malfunction. We present ATOMS (Accurate Timing prediction and Offloading for Mobile Systems), a framework to mitigate multi-tenant resource contention and to improve delay using a two-phase Plan-Schedule approach. The planning phase includes methods to predict future workloads from all clients, to estimate contention, and to devise offloading schedule to reduce contention. The scheduling phase dispatches arriving offloaded workload to the server machine that minimizes contention, based on the running workloads on each machine.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74995439","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}
Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.
{"title":"Towards automatic parameter tuning of stream processing systems","authors":"Muhammad Bilal, M. Canini","doi":"10.1145/3127479.3127492","DOIUrl":"https://doi.org/10.1145/3127479.3127492","url":null,"abstract":"Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74471045","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}
Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.
{"title":"A machine learning approach to live migration modeling","authors":"Changyeon Jo, Youngsu Cho, Bernhard Egger","doi":"10.1145/3127479.3129262","DOIUrl":"https://doi.org/10.1145/3127479.3129262","url":null,"abstract":"Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80072016","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}
Sagar Jha, J. Behrens, Theo Gkountouvas, Mae Milano, Weijia Song, E. Tremel, Sydney Zink, K. Birman, R. V. Renesse
The coming generation of Internet-of-Things (IoT) applications will process massive amounts of incoming data while supporting data mining and online learning. In cases with demanding real-time requirements, such systems behave as smart memories: a high-bandwidth service that captures sensor input, processes it using machine-learning tools, replicates and stores "interesting" data (discarding uninteresting content), updates knowledge models, and triggers urgently-needed responses. Derecho is a high-throughput library for building smart memories and similar services. At its core Derecho implements atomic multicast (Vertical Paxos) and state machine replication (the classic durable Paxos). Derecho's replicated template defines a replicated type; the corresponding objects are associated with subgroups, which can be sharded into key-value structures. The persistent and volatile storage templates implement version vectors with optional NVM persistence. These support time-indexed access, offering lock-free snapshot isolation that blends temporal precision and causal consistency. Derecho automates application management, supporting multigroup structures and providing consistent knowledge of the current membership mapping. A query can access data from many shards or subgroups, and consistency is guaranteed without any form of distributed locking. Whereas many systems run consensus on the critical path, Derecho requires consensus only when updating membership. By leveraging an RDMA data plane and NVM storage, and adopting a novel receiver-side batching technique, Derecho can saturate a 12.5GB RDMA network, sending millions of events per second in each subgroup or shard. In a single subgroup with 2--16 members, through-put peaks at 16 GB/s for large (100MB or more) objects. While key-value subgroups would typically use 2 or 3-member shards, unsharded subgroups could be large. In tests with a 128-member group, Derecho's multicast and Paxos protocols were just 3--5x slower than for a small group, depending on the traffic pattern. With network contention, slow members, or overlapping groups that generate concurrent traffic, Derecho's protocols remain stable and adapt to the available bandwidth.
{"title":"Building smart memories and high-speed cloud services for the internet of things with derecho","authors":"Sagar Jha, J. Behrens, Theo Gkountouvas, Mae Milano, Weijia Song, E. Tremel, Sydney Zink, K. Birman, R. V. Renesse","doi":"10.1145/3127479.3134597","DOIUrl":"https://doi.org/10.1145/3127479.3134597","url":null,"abstract":"The coming generation of Internet-of-Things (IoT) applications will process massive amounts of incoming data while supporting data mining and online learning. In cases with demanding real-time requirements, such systems behave as smart memories: a high-bandwidth service that captures sensor input, processes it using machine-learning tools, replicates and stores \"interesting\" data (discarding uninteresting content), updates knowledge models, and triggers urgently-needed responses. Derecho is a high-throughput library for building smart memories and similar services. At its core Derecho implements atomic multicast (Vertical Paxos) and state machine replication (the classic durable Paxos). Derecho's replicated template defines a replicated type; the corresponding objects are associated with subgroups, which can be sharded into key-value structures. The persistent and volatile storage templates implement version vectors with optional NVM persistence. These support time-indexed access, offering lock-free snapshot isolation that blends temporal precision and causal consistency. Derecho automates application management, supporting multigroup structures and providing consistent knowledge of the current membership mapping. A query can access data from many shards or subgroups, and consistency is guaranteed without any form of distributed locking. Whereas many systems run consensus on the critical path, Derecho requires consensus only when updating membership. By leveraging an RDMA data plane and NVM storage, and adopting a novel receiver-side batching technique, Derecho can saturate a 12.5GB RDMA network, sending millions of events per second in each subgroup or shard. In a single subgroup with 2--16 members, through-put peaks at 16 GB/s for large (100MB or more) objects. While key-value subgroups would typically use 2 or 3-member shards, unsharded subgroups could be large. In tests with a 128-member group, Derecho's multicast and Paxos protocols were just 3--5x slower than for a small group, depending on the traffic pattern. With network contention, slow members, or overlapping groups that generate concurrent traffic, Derecho's protocols remain stable and adapt to the available bandwidth.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75497175","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}
Kolbeinn Karlsson, Zhiming Shen, Weijia Song, Hakim Weatherspoon, R. V. Renesse, S. Wicker
The "cloud paradigm" can provide a wealth of sophisticated emergency communication services that are gamechangers in emergency response, but its current implementation is not suitable to the challenging environments in which these responses often take place. The networking infrastructure may be all but unavailable, and access to centralized datacenters may be impossible.
{"title":"Towards an emergency edge supercloud","authors":"Kolbeinn Karlsson, Zhiming Shen, Weijia Song, Hakim Weatherspoon, R. V. Renesse, S. Wicker","doi":"10.1145/3127479.3132253","DOIUrl":"https://doi.org/10.1145/3127479.3132253","url":null,"abstract":"The \"cloud paradigm\" can provide a wealth of sophisticated emergency communication services that are gamechangers in emergency response, but its current implementation is not suitable to the challenging environments in which these responses often take place. The networking infrastructure may be all but unavailable, and access to centralized datacenters may be impossible.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81196875","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 spite of many advantages of hybrid electrical/optical datacenter networks (Hybrid-DCN), current job schedulers for data-parallel frameworks are not suitable for Hybrid-DCN, since the schedulers do not aggregate data traffic to facilitate using optical circuit switch (OCS). We propose SchedOCS, a job scheduler for data-parallel frameworks in Hybrid-DCN that aims to take full advantage of the OCS to improve the job performance.
{"title":"Job scheduling for data-parallel frameworks with hybrid electrical/optical datacenter networks","authors":"Zhuozhao Li, Haiying Shen","doi":"10.1145/3127479.3132694","DOIUrl":"https://doi.org/10.1145/3127479.3132694","url":null,"abstract":"In spite of many advantages of hybrid electrical/optical datacenter networks (Hybrid-DCN), current job schedulers for data-parallel frameworks are not suitable for Hybrid-DCN, since the schedulers do not aggregate data traffic to facilitate using optical circuit switch (OCS). We propose SchedOCS, a job scheduler for data-parallel frameworks in Hybrid-DCN that aims to take full advantage of the OCS to improve the job performance.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83871025","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}
L. Suresh, P. Bodík, Ishai Menache, M. Canini, F. Ciucu
Multi-tenant distributed systems composed of small services, such as Service-oriented Architectures (SOAs) and Micro-services, raise new challenges in attaining high performance and efficient resource utilization. In these systems, a request execution spans tens to thousands of processes, and the execution paths and resource demands on different services are generally not known when a request first enters the system. In this paper, we highlight the fundamental challenges of regulating load and scheduling in SOAs while meeting end-to-end performance objectives on metrics of concern to both tenants and operators. We design Wisp, a framework for building SOAs that transparently adapts rate limiters and request schedulers system-wide according to operator policies to satisfy end-to-end goals while responding to changing system conditions. In evaluations against production as well as synthetic workloads, Wisp successfully enforces a range of end-to-end performance objectives, such as reducing average latencies, meeting deadlines, providing fairness and isolation, and avoiding system overload.
{"title":"Distributed resource management across process boundaries","authors":"L. Suresh, P. Bodík, Ishai Menache, M. Canini, F. Ciucu","doi":"10.1145/3127479.3132020","DOIUrl":"https://doi.org/10.1145/3127479.3132020","url":null,"abstract":"Multi-tenant distributed systems composed of small services, such as Service-oriented Architectures (SOAs) and Micro-services, raise new challenges in attaining high performance and efficient resource utilization. In these systems, a request execution spans tens to thousands of processes, and the execution paths and resource demands on different services are generally not known when a request first enters the system. In this paper, we highlight the fundamental challenges of regulating load and scheduling in SOAs while meeting end-to-end performance objectives on metrics of concern to both tenants and operators. We design Wisp, a framework for building SOAs that transparently adapts rate limiters and request schedulers system-wide according to operator policies to satisfy end-to-end goals while responding to changing system conditions. In evaluations against production as well as synthetic workloads, Wisp successfully enforces a range of end-to-end performance objectives, such as reducing average latencies, meeting deadlines, providing fairness and isolation, and avoiding system overload.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88403501","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}
N. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Burton J. Smith, R. Katz
Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.
{"title":"Selecting the best VM across multiple public clouds: a data-driven performance modeling approach","authors":"N. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Burton J. Smith, R. Katz","doi":"10.1145/3127479.3131614","DOIUrl":"https://doi.org/10.1145/3127479.3131614","url":null,"abstract":"Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88861757","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}
Metering is an important component of cloud database services. We discuss potential problems in verifiability for existing DBaaS metering and initiate a discussion of how we can address this problem.
{"title":"Towards verifiable metering for database as a service providers","authors":"Min Du, Ravishankar Ramamurthy","doi":"10.1145/3127479.3134349","DOIUrl":"https://doi.org/10.1145/3127479.3134349","url":null,"abstract":"Metering is an important component of cloud database services. We discuss potential problems in verifiability for existing DBaaS metering and initiate a discussion of how we can address this problem.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82594928","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}
Kenichi Yasukata, Felipe Huici, Vincenzo Maffione, G. Lettieri, Michio Honda
Network Function Virtualization has been touted as the silver bullet for tackling a number of operator problems, including vendor lock-in, fast deployment of new functionality, converged management, and lower expenditure since packet processing runs on inexpensive commodity servers. The reality, however, is that, in practice, it has proved hard to achieve the stable, predictable performance provided by hardware middleboxes, and so operators have essentially resorted to throwing money at the problem, deploying highly underutilized servers (e.g., one NF per CPU core) in order to guarantee high performance during peak periods and meet SLAs. In this work we introduce HyperNF, a high performance NFV framework aimed at maximizing server performance when concurrently running large numbers of NFs. To achieve this, HyperNF implements hypercall-based virtual I/O, placing packet forwarding logic inside the hypervisor to significantly reduce I/O synchronization overheads. HyperNF improves throughput by 10%-73% depending on the NF, is able to closely match resource allocation specifications (with deviations of only 3.5%), and to efficiently cope with changing traffic loads.
{"title":"HyperNF: building a high performance, high utilization and fair NFV platform","authors":"Kenichi Yasukata, Felipe Huici, Vincenzo Maffione, G. Lettieri, Michio Honda","doi":"10.1145/3127479.3127489","DOIUrl":"https://doi.org/10.1145/3127479.3127489","url":null,"abstract":"Network Function Virtualization has been touted as the silver bullet for tackling a number of operator problems, including vendor lock-in, fast deployment of new functionality, converged management, and lower expenditure since packet processing runs on inexpensive commodity servers. The reality, however, is that, in practice, it has proved hard to achieve the stable, predictable performance provided by hardware middleboxes, and so operators have essentially resorted to throwing money at the problem, deploying highly underutilized servers (e.g., one NF per CPU core) in order to guarantee high performance during peak periods and meet SLAs. In this work we introduce HyperNF, a high performance NFV framework aimed at maximizing server performance when concurrently running large numbers of NFs. To achieve this, HyperNF implements hypercall-based virtual I/O, placing packet forwarding logic inside the hypervisor to significantly reduce I/O synchronization overheads. HyperNF improves throughput by 10%-73% depending on the NF, is able to closely match resource allocation specifications (with deviations of only 3.5%), and to efficiently cope with changing traffic loads.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83853919","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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