IEEE Transactions on Cloud Computing最新文献

{"title":"BaaSLess: Backend-as-a-Service (BaaS)-Enabled Workflows in Federated Serverless Infrastructures","authors":"Thomas Larcher;Philipp Gritsch;Stefan Nastic;Sashko Ristov","doi":"10.1109/TCC.2024.3439268","DOIUrl":"10.1109/TCC.2024.3439268","url":null,"abstract":"Serverless is a popular paradigm for expressing compute-intensive applications as serverless workflows. In practice, a significant portion of the computing is typically offloaded to various Backend-as-a-Service (BaaS) cloud services. The recent rise of federated serverless and Sky computing offers cost and performance advantages for these BaaS-enabled serverless workflows. However, due to vendor lock-in and lack of service interoperability, many challenges remain that impact the development, deployment, and scheduling of BaaS-enabled serverless workflows in federated serverless infrastructures. This paper introduces \u0000<small>BaaSLess</small>\u0000 – a novel platform that delivers global and dynamic federated BaaS to serverless workflows. \u0000<small>BaaSLess</small>\u0000 provides: i) a novel SDK for uniform and dynamic access to federated BaaS services, reducing the complexity associated with the development of BaaS-enabled serverless workflows, ii) a novel globally-federated serverless BaaS framework that delivers a suite of BaaS-less ML services, including text-to-speech, speech-to-text, translation, and OCR, together with a globally-federated storage infrastructure, comprising AWS and Google cloud providers, and iii) a novel model and an algorithm for scheduling BaaS-enabled serverless workflows to improve their performance. Experimental results using three complementary BaaS-enabled serverless workflows show that \u0000<small>BaaSLess</small>\u0000 improves workflow execution time by up to \u0000<inline-formula><tex-math>$2.95times$</tex-math></inline-formula>\u0000 compared to the state-of-the-art serverless schedulers, often at a lower cost.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1088-1102"},"PeriodicalIF":5.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scalable and Write-Optimized Disaggregated B+-Tree With Adaptive Cache Assistance","authors":"Hang An;Fang Wang;Dan Feng;Xiaomin Zou;Zefeng Liu;Jianshun Zhang","doi":"10.1109/TCC.2024.3437472","DOIUrl":"10.1109/TCC.2024.3437472","url":null,"abstract":"Disaggregated memory (DM) architecture separates CPU and DRAM into computing/memory resource pools and interconnects them with high-speed networks. Storage systems on DM locate data by distributed index. However, existing distributed indexes either suffer from prohibitive synchronization overhead of write operation or sacrifice the performance of read operation, resulting in low throughput, high tail latency, and challenging trade-off. In this paper, we present Marlin+, a scalable and optimized B+-tree on DM. Marlin+ provides atomic granularity synchronization between write operations via three strategies: 1) a concurrent algorithm that is friendly to IDU operations (Insert, Delete, and Update), enabling different clients to concurrently operate on the same leaf node, 2) shared-exclusive leaf node lock, effectively preventing conflicts between index structure modification operation (SMO) and IDU operations, and 3) critical path compression of write to reduce latency of write operation. Moreover, Marlin+ proposes an adaptive remote address cache to accelerate the access of hot data. Compared to the state-of-the-art schemes based on DM, Marlin achieves 2.21× higher throughput and 83.4% lower P99 latency under YCSB hybrid workloads. Compared to Marlin, Marlin+ improves the throughput by up to 1.58× and reduces the P50 latency by up to 50.5% under YCSB read-intensive workloads.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1074-1087"},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885524","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}

{"title":"Sparkle: Deep Learning Driven Autotuning for Taming High-Dimensionality of Spark Deployments","authors":"Dimosthenis Masouros;George Retsinas;Sotirios Xydis;Dimitrios Soudris","doi":"10.1109/TCC.2024.3437484","DOIUrl":"10.1109/TCC.2024.3437484","url":null,"abstract":"The exponential growth of data in the Cloud has highlighted the need for more efficient data processing. In-Memory Computing frameworks (e.g., Spark) offer improved efficiency for large-scale data analytics, however, they also provide a plethora of configuration parameters that affect the resource consumption and performance of applications. Manually optimizing these parameters is a time-consuming process, due to \u0000<i>i)</i>\u0000 the high-dimensional configuration space, \u0000<i>ii)</i>\u0000 the complex inter-relationship between different parameters, \u0000<i>iii)</i>\u0000 the diverse nature of workloads and \u0000<i>iv)</i>\u0000 the inherent data heterogeneity. We introduce \u0000<i>Sparkle</i>\u0000, an end-to-end deep learning-based framework for automating the performance modeling and tuning of Spark applications. We introduce a modular DNN architecture that expands to the entire Spark parameter configuration space and provides a universal performance modeling approach, completely eliminating the need for human or statistical reasoning. By employing a genetic optimization process, \u0000<i>Sparkle</i>\u0000 quickly traverses the design space and identifies highly optimized Spark configurations. Our experiments on the HiBench benchmark suite show that \u0000<i>Sparkle</i>\u0000 delivers an average prediction accuracy of 93%, with high generalization capabilities, i.e., \u0000<inline-formula><tex-math>$approx 80%$</tex-math></inline-formula>\u0000 accuracy for unseen workloads, dataset sizes and configurations, outperforming state-of-art. Regarding end-to-end optimization, \u0000<i>Sparkle</i>\u0000 efficiently explores Spark's high-dimensional parameter space, delivering new dominant Spark configurations, which correspond to 65% Pareto coverage w.r.t its Spark native optimization counterpart.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1058-1073"},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885525","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}

{"title":"MFSSE: Multi-Keyword Fuzzy Ranked Symmetric Searchable Encryption With Pattern Hidden in Mobile Cloud Computing","authors":"Dajiang Chen;Zeyu Liao;Zhidong Xie;Ruidong Chen;Zhen Qin;Mingsheng Cao;Hong-Ning Dai;Kuan Zhang","doi":"10.1109/TCC.2024.3430237","DOIUrl":"10.1109/TCC.2024.3430237","url":null,"abstract":"In this paper, we propose a novel Multi-keyword Fuzzy Symmetric Searchable Encryption (SSE) with patterns hidden, namely MFSSE. In MFSSE, the search trapdoor can be modified differently each time even if the keywords are the same when performing multi-keyword search to prevent the leakage of search patterns. Moreover, MFSSE modifies the search trapdoor by introducing random false negative and false positive errors to resist access pattern leakage. Furthermore, MFSSE utilizes efficient cryptographic algorithms (e.g., Locality-Sensitive Hashing) and lightweight operations (such as, integer addition, matrix multiplication, etc.) to minimize computational and communication, and storage overheads on mobile devices while meeting security and functional requirements. Specifically, its query process requires only a single round of communication, in which, the communication cost is linearly related to the number of the documents in the database, and is independent of the total number of keywords and the number of queried keywords; its computational complexity for matching a document is \u0000<inline-formula><tex-math>$O(1)$</tex-math></inline-formula>\u0000; and it requires only a small amount of fixed local storage (i.e., secret key) to be suitable for mobile scenarios. The experimental results demonstrate that MFSSE can prevent the leakage of access patterns and search patterns, while keeping a low communication and computation overheads.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1042-1057"},"PeriodicalIF":5.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740686","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}

{"title":"Security, Reliability, Cost, and Energy-Aware Scheduling of Real-Time Workflows in Compute-Continuum Environments","authors":"Ahmad Taghinezhad-Niar;Javid Taheri","doi":"10.1109/TCC.2024.3426282","DOIUrl":"10.1109/TCC.2024.3426282","url":null,"abstract":"Emerging computing paradigms like mist, edge, and fog computing address challenges in the real-time processing of vast Internet of Things (IoT) applications. Alongside, cloud computing offers a suitable platform for executing services. Together, they form a multi-tier computing environment known as compute-continuum to efficiently enhance data management and task execution of real-time tasks. The primary considerations for compute-continuum include variations in resource configuration and network architecture, rental cost model, application security needs, energy consumption, transmission latency, and system reliability. To address these problems, we propose two scheduling algorithms (RCSECH and RSECH) for real-time multi-workflow scheduling frameworks. Both algorithms optimize for rental cost, energy consumption, and task reliability when scheduling real-time workflows while considering deadlines and security requirements as constraints. RCSECH also factors in reliability alongside these constraints. The environment under investigation consists of a compute-continuum architecture consisting of mist, edge, fog, and cloud layers, each potentially composed of heterogeneous resources. The framework undergoes evaluation via simulation experiments, revealing promising results. Specifically, the framework exhibits the capability to enhance reliability by up to 7%, reduce energy consumption by 8%, surpass reliability constraints by more than 25%, and generate cost savings by at least 15%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"954-965"},"PeriodicalIF":5.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585945","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}

{"title":"$varepsilon$ɛ-LAP: A Lightweight and Adaptive Cache Partitioning Scheme With Prudent Resizing Decisions for Content Delivery Networks","authors":"Peng Wang;Yu Liu;Ziqi Liu;Zhelong Zhao;Ke Liu;Ke Zhou;Zhihai Huang","doi":"10.1109/TCC.2024.3420454","DOIUrl":"10.1109/TCC.2024.3420454","url":null,"abstract":"As dependence on Content Delivery Networks (CDNs) increases, there is a growing need for innovative solutions to optimize cache performance amid increasing traffic and complicated cache-sharing workloads. Allocating exclusive resources to applications in CDNs boosts the overall cache hit ratio (OHR), enhancing efficiency. However, the traditional method of creating the miss ratio curve (MRC) is unsuitable for CDNs due to the diverse sizes of items and the vast number of applications, leading to high computational overhead and performance inconsistency. To tackle this issue, we propose a \u0000<u>l</u>\u0000ightweight and \u0000<u>a</u>\u0000daptive cache \u0000<u>p</u>\u0000artitioning scheme called \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP. This scheme uses a corresponding shadow cache for each partition and sorts them based on the average hit numbers on the granularity unit in the shadow caches. During partition resizing, \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP transfers storage capacity, measured in units of granularity, from the \u0000<inline-formula><tex-math>$(N-k+1)$</tex-math></inline-formula>\u0000-th (\u0000<inline-formula><tex-math>$kleq frac{N}{2}$</tex-math></inline-formula>\u0000) partition to the \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-th partition. A learning threshold parameter, i.e., \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000, is also introduced to prudently determine when to resize partitions, improving caching efficiency. This can eliminate about 96.8% of unnecessary partition resizing without compromising performance. \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP, when deployed in \u0000<i>PicCloud</i>\u0000 at \u0000<i>Tencent</i>\u0000, improved OHR by 9.34% and reduced the average user access latency by 12.5 ms. Experimental results show that \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP outperforms other cache partitioning schemes in terms of both OHR and access latency, and it effectively adapts to workload variations.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"942-953"},"PeriodicalIF":5.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503565","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}

{"title":"Secure and Flexible Coded Distributed Matrix Multiplication Based on Edge Computing for Industrial Metaverse","authors":"Houming Qiu;Kun Zhu;Dusit Niyato","doi":"10.1109/TCC.2024.3415165","DOIUrl":"10.1109/TCC.2024.3415165","url":null,"abstract":"The Industrial Metaverse is driving a new revolution wave for smart manufacturing domain by reproducing the real industrial environment in a virtual space. Real-time synchronization and rendering of all industrial factors result in numerous time-sensitive and computation-intensive tasks, especially matrix multiplication. Distributed edge computing (DEC) can be exploited to handle these tasks due to its low-latency and powerful computing. In this paper, we propose an efficient and reliable coded DEC framework to compute large-scale matrix multiplication tasks. However, an existence of stragglers causes high computation latency that seriously limits the application of DEC in the Industrial Metaverse. To mitigate the impact of stragglers, we design a secure and flexible PolyDot (SFPD) code, which enables information theoretic security (ITS) protection. Several improvements can be achieved with the proposed SFPD. First, it can achieve a smaller recovery threshold than that of the existing codes in almost all settings. And compared with the original PolyDot codes, our SFPD code considers the extra workers required to add ITS protection. It also provides a flexible tradeoff between recovery threshold and communication & computation loads by simply adjusting two given storage parameters \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$t$</tex-math></inline-formula>\u0000. Furthermore, as an important application scenario, the SFPD code is employed to secure model training in machine learning, which can alleviate the straggler effects and protect ITS of raw data. The experiments demonstrate that the SFPD code can significantly speed up the training process while providing ITS of data. Finally, we provide comprehensive performance analysis which shows the superiority of the SFPD code.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1026-1041"},"PeriodicalIF":5.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937583","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}

{"title":"Non-Clairvoyant Scheduling of Distributed Machine Learning With Inter-Job and Intra-Job Parallelism on Heterogeneous GPUs","authors":"Fahao Chen;Peng Li;Celimuge Wu;Song Guo","doi":"10.1109/TCC.2024.3414440","DOIUrl":"10.1109/TCC.2024.3414440","url":null,"abstract":"Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we propose \u0000<italic>Hare</i>\u0000, a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster. \u0000<italic>Hare</i>\u0000 adopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Given full knowledge of job arrival time and sizes, we propose a fast heuristic algorithm to minimize the average job completion time and derive its theoretical bound is derived. Without prior knowledge of jobs, we propose an online algorithm based on the Heterogeneity-aware Least-Attained Service (HLAS) policy. We evaluate \u0000<italic>Hare</i>\u0000 using a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art, achieving a performance improvement of about 2.94×.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1011-1025"},"PeriodicalIF":5.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937581","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}

{"title":"An Adaptive Cloud Resource Quota Scheme Based on Dynamic Portraits and Task-Resource Matching","authors":"Zuodong Jin;Dan Tao;Peng Qi;Ruipeng Gao","doi":"10.1109/TCC.2024.3410390","DOIUrl":"10.1109/TCC.2024.3410390","url":null,"abstract":"Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"996-1010"},"PeriodicalIF":5.3,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937582","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}

{"title":"Multi-Data Center Tie-Line Power Smoothing Method Based on Demand Response","authors":"Ting Yang;Yuxing Hou;Shaotang Cai;Jie Yu;Haibo Pen","doi":"10.1109/TCC.2024.3410377","DOIUrl":"10.1109/TCC.2024.3410377","url":null,"abstract":"Geographically distributed data centers (DCs) have emerged as significant energy consumers, which has led to the integration of renewable energy sources (RES) into DC power provisioning systems. However, the intermittent nature of RES and the randomness of user requests can cause significant fluctuations in DC operating power. It can be detrimental to the operation of IT equipment and lead to instability in the power grid. In this paper, aiming for tightly coupled interconnection scenarios with multi-data centers in varying regions, a multi-data center tie-line power smoothing method based on demand response is proposed. By modulating the power load of server clusters with workload scheduling, we establish a control model combined with intra-DC temporal task migration and inter-DC spatial task migration to deal with high-frequency power fluctuations. The uninterruptible power supply (UPS) battery control model is established to tackle low-frequency fluctuations. Furthermore, we design the two-stage heuristic power regulation algorithm to achieve the best practice of smoothing effect by real-time tracking of power targets after two-layer filtering. Finally, this paper performs a detailed performance simulation evaluation based on tracking data from a real DC and wind and photovoltaic (PV) new energy generation data, using four interconnected DC parks of different sizes across different regions as examples. The simulation results demonstrate that the proposed method effectively smoothing the multi-data center's tie-line power. Additionally, inter-DC temporal task migration serves as a viable solution to overcome the limitations of task migration response within a single DC, reducing the frequency of UPS battery bank charges and discharges, which in turn prolongs their service life. This approach facilitates the utilization of RES while maintaining power quality, and it also aids in reducing the escalating operation and maintenance expenses of DCs.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"983-995"},"PeriodicalIF":5.3,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937598","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}