IEEE Transactions on Cloud Computing最新文献_第6页

A Method to Compare Scaling Algorithms for Cloud-Based Services 一种比较基于云的服务缩放算法的方法

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-18 DOI: 10.1109/TCC.2024.3500139

Danny De Vleeschauwer;Chia-Yu Chang;Paola Soto;Yorick De Bock;Miguel Camelo;Koen De Schepper

引用次数: 0

COCSN: A Multi-Tiered Cascaded Optical Circuit Switching Network for Data Center COCSN：用于数据中心的多层级联光路交换网络

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-11 DOI: 10.1109/TCC.2024.3488275

Shuo Li;Huaxi Gu;Xiaoshan Yu;Hua Huang;Songyan Wang;Zeshan Chang

{"title":"COCSN: A Multi-Tiered Cascaded Optical Circuit Switching Network for Data Center","authors":"Shuo Li;Huaxi Gu;Xiaoshan Yu;Hua Huang;Songyan Wang;Zeshan Chang","doi":"10.1109/TCC.2024.3488275","DOIUrl":"https://doi.org/10.1109/TCC.2024.3488275","url":null,"abstract":"A cascaded network represents a classic scaling-out model in traditional electrical switching networks. Recent proposals have integrated optical circuit switching at specific tiers of these networks to reduce power consumption and enhance topological flexibility. Utilizing a multi-tiered cascaded optical circuit switching network is expected to extend the advantages of optical circuit switching further. The main challenges fall into two categories. First, an architecture with sufficient connectivity is required to support varying workloads. Second, the network reconfiguration is more complex and necessitates a low-complexity scheduling algorithm. In this work, we propose COCSN, a multi-tiered cascaded optical circuit switching network architecture for data center. COCSN employs wavelength-selective switches that integrate multiple wavelengths to enhance network connectivity. We formulate a mathematical model covering lightpath establishment, network reconfiguration, and reconfiguration goals, and propose theorems to optimize the model. Based on the theorems, we introduce an over-subscription-supported wavelength-by-wavelength scheduling algorithm, facilitating agile establishment of lightpaths in COCSN tailored to communication demand. This algorithm effectively addresses scheduling complexities and mitigates the issue of lengthy WSS configuration times. Simulation studies investigate the impact of flow length, WSS reconfiguration time, and communication domain on COCSN, verifying its significantly lower complexity and superior performance over classical cascaded networks.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1463-1475"},"PeriodicalIF":5.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Granularity Federated Learning by Graph-Partitioning 基于图分区的多粒度联邦学习

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-11-11 DOI: 10.1109/TCC.2024.3494765

Ziming Dai;Yunfeng Zhao;Chao Qiu;Xiaofei Wang;Haipeng Yao;Dusit Niyato

{"title":"Multi-Granularity Federated Learning by Graph-Partitioning","authors":"Ziming Dai;Yunfeng Zhao;Chao Qiu;Xiaofei Wang;Haipeng Yao;Dusit Niyato","doi":"10.1109/TCC.2024.3494765","DOIUrl":"https://doi.org/10.1109/TCC.2024.3494765","url":null,"abstract":"In edge computing, energy-limited distributed edge clients present challenges such as heterogeneity, high energy consumption, and security risks. Traditional blockchain-based federated learning (BFL) struggles to address all three of these challenges simultaneously. This article proposes a Graph-Partitioning Multi-Granularity Federated Learning method on a consortium blockchain, namely GP-MGFL. To reduce the overall communication overhead, we adopt a balanced graph partitioning algorithm while introducing observer and consensus nodes. This method groups clients to minimize high-cost communications and focuses on the guidance effect within each group, thereby ensuring effective guidance with reduced overhead. To fully leverage heterogeneity, we introduce a cross-granularity guidance mechanism. This mechanism involves fine-granularity models guiding coarse-granularity models to enhance the accuracy of the latter models. We also introduce a credit model to adjust the contribution of models to the global model dynamically and to dynamically select leaders responsible for model aggregation. Finally, we implement a prototype system on real physical hardware and compare it with several baselines. Experimental results show that the accuracy of the GP-MGFL algorithm is 5.6% higher than that of ordinary BFL algorithms. In addition, compared to other grouping methods, such as greedy grouping, the accuracy of the proposed method improves by about 1.5%. In scenarios with malicious clients, the maximum accuracy improvement reaches 11.1%. We also analyze and summarize the impact of grouping and the number of clients on the model, as well as the impact of this method on the inherent security of the blockchain itself.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"18-33"},"PeriodicalIF":5.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Renewable Energy Utilization in Cloud Data Centers Through Dynamic Overbooking: An MDP-Based Approach 通过动态超额预订优化云数据中心的可再生能源利用：基于 MDP 的方法

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-30 DOI: 10.1109/TCC.2024.3487954

Tuhin Chakraborty;Carlo Kopp;Adel N. Toosi

{"title":"Optimizing Renewable Energy Utilization in Cloud Data Centers Through Dynamic Overbooking: An MDP-Based Approach","authors":"Tuhin Chakraborty;Carlo Kopp;Adel N. Toosi","doi":"10.1109/TCC.2024.3487954","DOIUrl":"https://doi.org/10.1109/TCC.2024.3487954","url":null,"abstract":"The shift towards renewable energy sources for powering data centers is increasingly important in the era of cloud computing. However, integrating renewable energy sources into cloud data centers presents a challenge due to their variable and intermittent nature. The unpredictable workload demands in cloud data centers further complicate this problem. In response to this pressing challenge, we propose a novel approach in this paper: adapting the workload to match the renewable energy supply. Our solution involves dynamic overbooking of resources, providing energy flexibility to data center operators. We propose a framework that stochastically models both workload and energy source information, leveraging Markov Decision Processes (MDP) to determine the optimal overbooking degree based on the workload flexibility of data center clients. We validate the proposed algorithm in realistic settings through extensive simulations. Results demonstrate the superiority of our proposed method over existing approaches, achieving better matching with the renewable energy supply by 55.6%, 34.65%, and 40.7% for workload traces from <italic>Nectar</i> Cloud, <italic>Google</i>, and <italic>Wikipedia</i>, respectively.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 1","pages":"1-17"},"PeriodicalIF":5.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aggregate Monitoring for Geo-Distributed Kubernetes Cluster Federations 针对地理分布式 Kubernetes 集群联盟的聚合监控

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-17 DOI: 10.1109/TCC.2024.3482574

Chih-Kai Huang;Guillaume Pierre

{"title":"Aggregate Monitoring for Geo-Distributed Kubernetes Cluster Federations","authors":"Chih-Kai Huang;Guillaume Pierre","doi":"10.1109/TCC.2024.3482574","DOIUrl":"https://doi.org/10.1109/TCC.2024.3482574","url":null,"abstract":"Distributed monitoring is an essential functionality to allow large cluster federations to efficiently schedule applications on a set of available geo-distributed resources. However, periodically reporting the precise status of each available server is both unnecessary to allow accurate scheduling and unscalable when the number of servers grows. This paper proposes Acala, an aggregate monitoring framework for geo-distributed Kubernetes cluster federations which aims to provide the management cluster with aggregated information about the entire cluster instead of individual servers. Based on actual deployment under a controlled environment in the geo-distributed Grid’5000 testbed, our evaluations show that Acala reduces the cross-cluster network traffic by up to 97% and the scrape duration by up to 55% in the single member cluster experiment. Our solution also decreases cross-cluster network traffic by 95% and memory resource consumption by 83% in multiple member cluster scenarios. A comparison of scheduling efficiency with and without data aggregation shows that aggregation has minimal effects on the system’s scheduling function. These results indicate that our approach is superior to the existing solution and is suitable to handle large-scale geo-distributed Kubernetes cluster federation environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1449-1462"},"PeriodicalIF":5.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Group Formation and Sampling in Group-Based Hierarchical Federated Learning 基于群体的分层联邦学习中的群体形成与抽样

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-17 DOI: 10.1109/TCC.2024.3482865

Jiyao Liu;Xuanzhang Liu;Xinliang Wei;Hongchang Gao;Yu Wang

{"title":"Group Formation and Sampling in Group-Based Hierarchical Federated Learning","authors":"Jiyao Liu;Xuanzhang Liu;Xinliang Wei;Hongchang Gao;Yu Wang","doi":"10.1109/TCC.2024.3482865","DOIUrl":"https://doi.org/10.1109/TCC.2024.3482865","url":null,"abstract":"Hierarchical federated learning has emerged as a pragmatic approach to addressing scalability, robustness, and privacy concerns within distributed machine learning, particularly in the context of edge computing. This hierarchical method involves grouping clients at the edge, where the constitution of client groups significantly impacts overall learning performance, influenced by both the benefits obtained and costs incurred during group operations (such as group formation and group training). This is especially true for edge and mobile devices, which are more sensitive to computation and communication overheads. The formation of groups is critical for group-based hierarchical federated learning but often neglected by researchers, especially in the realm of edge systems. In this paper, we present a comprehensive exploration of a group-based federated edge learning framework utilizing the hierarchical cloud-edge-client architecture and employing probabilistic group sampling. Our theoretical analysis of its convergence rate, considering the characteristics of client groups, reveals the pivotal role played by group heterogeneity in achieving convergence. Building on this insight, we introduce new methods for group formation and group sampling, aiming to mitigate data heterogeneity within groups and enhance the convergence and overall performance of federated learning. Our proposed methods are validated through extensive experiments, demonstrating their superiority over current algorithms in terms of prediction accuracy and training cost.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1433-1448"},"PeriodicalIF":5.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded Systems HEXO：在低成本、低功耗嵌入式系统上卸载长时间运行的计算和内存密集型工作负载

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-16 DOI: 10.1109/TCC.2024.3482178

Pierre Olivier;A K M Fazla Mehrab;Sandeep Errabelly;Stefan Lankes;Mohamed Lamine Karaoui;Robert Lyerly;Sang-Hoon Kim;Antonio Barbalace;Binoy Ravindran

{"title":"HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded Systems","authors":"Pierre Olivier;A K M Fazla Mehrab;Sandeep Errabelly;Stefan Lankes;Mohamed Lamine Karaoui;Robert Lyerly;Sang-Hoon Kim;Antonio Barbalace;Binoy Ravindran","doi":"10.1109/TCC.2024.3482178","DOIUrl":"https://doi.org/10.1109/TCC.2024.3482178","url":null,"abstract":"OS-capable embedded systems exhibiting a very low power consumption are available at an extremely low price point. It makes them highly compelling in a datacenter context. We show that sharing long-running, compute-intensive datacenter workloads between a server machine and one or a few connected embedded boards of negligible cost and power consumption can yield significant performance and energy benefits. Our approach, named Heterogeneous EXecution Offloading (HEXO), selectively offloads Virtual Machines (VMs) from server-class machines to embedded boards. Our design tackles several challenges. We address the Instruction Set Architecture (ISA) difference between typical servers (x86) and embedded systems (ARM) through hypervisor and guest OS-level support for heterogeneous-ISA runtime VM migration. We cope with the low amount of resources in embedded systems by using lightweight VMs – unikernels – and by using the server's free RAM as remote memory for embedded boards through a transparent lightweight memory disaggregation mechanism for heterogeneous server-embedded clusters, called Netswap. VMs are offloaded based on an estimation of the slowdown expected from running on a given board. We build a prototype of HEXO and demonstrate significant increases in throughput (up to 67%) and energy efficiency (up to 56%) using benchmarks representative of compute-intensive long-running workloads.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1415-1432"},"PeriodicalIF":5.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Offloading and Resource Allocation for Collaborative Cloud Computing With Dependent Subtask Scheduling on Multi-Core Server 基于子任务调度的多核协同云计算联合卸载与资源分配

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-15 DOI: 10.1109/TCC.2024.3481039

Zihan Gao;Peixiao Zheng;Wanming Hao;Shouyi Yang

{"title":"Joint Offloading and Resource Allocation for Collaborative Cloud Computing With Dependent Subtask Scheduling on Multi-Core Server","authors":"Zihan Gao;Peixiao Zheng;Wanming Hao;Shouyi Yang","doi":"10.1109/TCC.2024.3481039","DOIUrl":"https://doi.org/10.1109/TCC.2024.3481039","url":null,"abstract":"Collaborative cloud computing (CCC) has emerged as a promising paradigm to support computation-intensive and delay-sensitive applications by leveraging MEC and MCC technologies. However, the coupling between multiple variables and subtask dependencies within an application poses significant challenges to the computation offloading mechanism. To address this, we investigate the computation offloading problem for CCC by jointly optimizing offloading decisions, resource allocation, and subtask scheduling across a multi-core edge server. First, we exploit latency to design a subtask dependency model within the application. Next, we formulate a System Energy-Time Cost (\u0000<inline-formula><tex-math>$SETC$</tex-math></inline-formula>\u0000) minimization problem that considers the trade-off between time and energy consumption while satisfying subtask dependencies. Due to the complexity of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Maximum Local Searching Offloading (MLSO) and Sequential Searching Offloading (SSO), to jointly optimize offloading decisions and resource allocation. We then model dependent subtask scheduling across the multi-core edge server as a Job-Shop Scheduling Problem (JSSP) and propose a Genetic-based Task Scheduling (GTS) algorithm to achieve optimal dependent subtask scheduling on the multi-core edge server. Finally, our simulation results demonstrate the effectiveness of the proposed MLSO, SSO, and GTS algorithms under different parameter settings.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1401-1414"},"PeriodicalIF":5.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAM: A Resource-Aware DDoS Attack Mitigation Framework in Clouds RAM：云中的资源感知型DDoS攻击缓解框架

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-14 DOI: 10.1109/TCC.2024.3480194

Fangyuan Xing;Fei Tong;Jialong Yang;Guang Cheng;Shibo He

{"title":"RAM: A Resource-Aware DDoS Attack Mitigation Framework in Clouds","authors":"Fangyuan Xing;Fei Tong;Jialong Yang;Guang Cheng;Shibo He","doi":"10.1109/TCC.2024.3480194","DOIUrl":"https://doi.org/10.1109/TCC.2024.3480194","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks threaten cloud servers by flooding redundant requests, leading to system resource exhaustion and legitimate service shutdown. Existing DDoS attack mitigation mechanisms mainly rely on resource expansion, which may result in unexpected resource over-provisioning and accordingly increase cloud system costs. To effectively mitigate DDoS attacks without consuming extra resources, the main challenges lie in the compromisesbetween incoming requests and available cloud resources. This paper proposes a resource-aware DDoS attack mitigation framework named RAM, where the mechanism of feedback in control theory is employed to adaptively adjust the interaction between incoming requests and available cloud resources. Specifically, two indicators including request confidence level and maximum cloud workload are designed. In terms of these two indicators, the incoming requests will be classified using proportional-integral-derivative (PID) feedback control-based classification scheme with request determination adaptation. The incoming requests can be subsequently processed according to their confidence levels as well as the workload and available resources of cloud servers, which achieves an effective resource-aware mitigation of DDoS attacks. Extensive experiments have been conducted to verify the effectiveness of RAM, which demonstrate that the proposed RAM can improve the request classification performance and guarantee the quality of service.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1387-1400"},"PeriodicalIF":5.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and Computation Offloading 基于联合部署和卸载的无人机辅助移动边缘计算响应延迟最小化

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-10-10 DOI: 10.1109/TCC.2024.3478172

Jianshan Zhang;Haibo Luo;Xing Chen;Hong Shen;Longkun Guo

{"title":"Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and Computation Offloading","authors":"Jianshan Zhang;Haibo Luo;Xing Chen;Hong Shen;Longkun Guo","doi":"10.1109/TCC.2024.3478172","DOIUrl":"https://doi.org/10.1109/TCC.2024.3478172","url":null,"abstract":"As a promising technique for offloading computation tasks from mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) utilizes UAVs as computational resources. A popular method for enhancing the quality of service (QoS) of UAV-assisted MEC systems is to jointly optimize UAV deployment and computation task offloading. This imposes the challenge of dynamically adjusting UAV deployment and computation offloading to accommodate the changing positions and computational requirements of mobile devices. Due to the real-time requirements of MEC computation tasks, finding an efficient joint optimization approach is imperative. This paper proposes an algorithm aimed at minimizing the average response delay in a UAV-assisted MEC system. The approach revolves around the joint optimization of UAV deployment and computation offloading through convex optimization. We break down the problem into three sub-problems: UAV deployment, Ground Device (GD) access, and computation tasks offloading, which we address using the block coordinate descent algorithm. Observing the \u0000<inline-formula><tex-math>$NP$</tex-math></inline-formula>\u0000-hardness nature of the original problem, we present near-optimal solutions to the decomposed sub-problems. Simulation results demonstrate that our approach can generate a joint optimization solution within seconds and diminish the average response delay compared to state-of-the-art algorithms and other advanced algorithms, with improvements ranging from 4.70% to 42.94%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1372-1386"},"PeriodicalIF":5.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0