IEEE Transactions on Cloud Computing最新文献_第3页

Delegatable Multi-Authority Attribute-Based Anonymous Credentials 可委派的基于多授权机构属性的匿名凭证

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-28 DOI: 10.1109/TCC.2025.3555519

Meng Sun;Junzuo Lai;Xiaohan Mo;Chi Wu;Peng Li;Cheng-Kang Chu;Robert H. Deng

引用次数: 0

ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing ReflexPilot：基于深度强化学习的边缘云协同计算启动感知依赖任务调度

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-27 DOI: 10.1109/TCC.2025.3555231

Wenhao Zou;Zongshuai Zhang;Nina Wang;Yu Tian;Lin Tian

{"title":"ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing","authors":"Wenhao Zou;Zongshuai Zhang;Nina Wang;Yu Tian;Lin Tian","doi":"10.1109/TCC.2025.3555231","DOIUrl":"https://doi.org/10.1109/TCC.2025.3555231","url":null,"abstract":"With the increasing number of devices, the demand for data computation is growing rapidly. In edge-cloud collaborative computing, tasks can be scheduled to servers as interdependent subtasks, enhancing performance through parallel computing. A task is executed in an executor, which must first initialize the runtime environment in a process called task startup. However, most existing research neglects the reuse of executors, leading to considerable delays during task startup. To address this issue, we model the edge-cloud collaborative task scheduling scenario considering executor reuse, task startup, and dependency relationships. We then formulate the dependent task scheduling problem with task startup. To meet real-time demands in edge-cloud collaborative computing, we propose ReflexPilot, an online task scheduling architecture featuring executor management. Building on this architecture, we introduce OTSA-PPO, a task scheduling algorithm based on Proximal Policy Optimization (PPO), and EMA, an advanced executor allocation algorithm. Under constraints of computational and communication resources, ReflexPilot leverages OTSA-PPO for online scheduling of dependent tasks based on current states, while EMA pre-creates and reuses executors to reduce the average task completion time. Extensive simulations demonstrate that ReflexPilot significantly reduces the average task completion time by 31% to 71% compared with existing baselines.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"641-654"},"PeriodicalIF":5.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232045","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

BPFGuard: Multi-Granularity Container Runtime Mandatory Access Control BPFGuard：多粒度容器运行时强制访问控制

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-24 DOI: 10.1109/TCC.2025.3551838

Hui Lu;Xiaojiang Du;Dawei Hu;Shen Su;Zhihong Tian

{"title":"BPFGuard: Multi-Granularity Container Runtime Mandatory Access Control","authors":"Hui Lu;Xiaojiang Du;Dawei Hu;Shen Su;Zhihong Tian","doi":"10.1109/TCC.2025.3551838","DOIUrl":"https://doi.org/10.1109/TCC.2025.3551838","url":null,"abstract":"The adoption of container-based cloud computing services has been prevalent, especially with the introduction of Kubernetes, which enables the automated deployment, scaling, and administration of applications in containers, hence boosting the popularity of containers. As a result, researchers have placed greater emphasis on container runtime security, notably investigating the efficacy of traditional techniques such as Capabilities, Seccomp, and Linux security modules in guaranteeing container security. However, due to the limitations imposed by the container environment, the results have been unsatisfactory. In addition, eBPF-based solutions face the problem of being unable to quickly load policies and affect real-time operations when faced with newer kernel vulnerabilities. This paper investigates the limitations of existing container security mechanisms. Additionally, it examines the specific constraints of these mechanisms in Kubernetes environments. The paper classifies container monitoring and obligatory access control into three distinct categories: system call access control, LSM hook access control, and kernel function access control. Therefore, we propose a technique for regulating container access with a variety of granularity levels. This technique is executed using eBPF and is tightly integrated with Kubernetes to collect relevant meta-information. In addition, we suggest implementing a consolidated routing method and employing function tail call chaining to overcome the limitation of eBPF in enforcing mandatory access control for containers. Lastly, we conducted a series of experiment to verify the effectiveness of the system's security using CVE-2022-0492 and to benchmark the system that had BPFGuard enabled. The results indicate that the average performance loss increased merely by 2.16%, demonstrating that there are no adverse effects on the container services. This suggests that greater security can be achieved at a minimal cost.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"629-640"},"PeriodicalIF":5.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229455","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

Leakage Reduced Searchable Symmetric Encryption for Multi-Keyword Queries 多关键字查询的减少泄漏可搜索对称加密

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-23 DOI: 10.1109/TCC.2025.3573378

Qinghua Deng;Lanxiang Chen;Yizhao Zhu;Yi Mu

{"title":"Leakage Reduced Searchable Symmetric Encryption for Multi-Keyword Queries","authors":"Qinghua Deng;Lanxiang Chen;Yizhao Zhu;Yi Mu","doi":"10.1109/TCC.2025.3573378","DOIUrl":"https://doi.org/10.1109/TCC.2025.3573378","url":null,"abstract":"Conjunctive keyword queries on untrusted cloud servers represent one of the most common forms of search in encrypted environments. Extensive research has been devoted to developing efficient schemes that support multi-keyword queries. In particular, the Oblivious Cross-Tags (OXT) protocol has received significant attention and is widely regarded as a benchmark in this domain. However, existing schemes fail to simultaneously hide the Keyword-Pair Result Pattern (KPRP) and the conditional Intersection Pattern (IP), potentially leaking additional information to the server. In this work, we propose a novel searchable symmetric encryption (SSE) scheme, referred to as <italic>Result Hiding Search (RHS)</i>, which aims to minimize result pattern leakage and achieve query result hiding during the index retrieval phase by integrating Private Set Intersection (PSI) techniques. Our scheme enhances privacy by employing PSI for secure membership testing. To improve query efficiency, we shift the expensive complex computation to the offline phase, and utilize efficient pseudorandom functions and hash functions during the online phase. Moreover, we propose a variant of RHS, called vRHS, designed to reduce client-side storage overhead. A simulation-based security proof demonstrates that our scheme is robust against non-adaptive adversaries. Comprehensive experimental evaluation further shows that our approach achieves better security and efficiency trade-offs compared to existing SSE schemes.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"882-894"},"PeriodicalIF":5.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998280","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

T-COMS: A Time-Slot-Aware and Cost-Effective Data Transfer Method for Geo-Distributed Data Centers T-COMS：一种地理分布数据中心的时隙感知和经济有效的数据传输方法

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-21 DOI: 10.1109/TCC.2025.3572308

Bita Fatemipour;Zhe Zhang;Marc St-Hilaire

{"title":"T-COMS: A Time-Slot-Aware and Cost-Effective Data Transfer Method for Geo-Distributed Data Centers","authors":"Bita Fatemipour;Zhe Zhang;Marc St-Hilaire","doi":"10.1109/TCC.2025.3572308","DOIUrl":"https://doi.org/10.1109/TCC.2025.3572308","url":null,"abstract":"With the increasing demands placed on geographically distributed Data Centers (DCs), recent studies have focused on optimizing performance from the perspective of both cloud providers and customers. These studies address a variety of goals, such as minimizing transmission time, reducing resource usage, and optimizing network costs. However, many existing models for workload transfers operate using a uniform time-slot approach, which limits their flexibility in handling variable data transfer requests with different deadline requirements. This lack of adaptability can negatively impact the quality of service for users. Additionally, these models often overlook the potential benefits of incorporating multiple data sources, which can lead to sub-optimal transmission times. To overcome these limitations, this paper introduces T-COMS, a Time-slot-aware, COst-effective, and Multi-Source-aware method for file transfers tailored specifically for geo-distributed DCs, leveraging a multi-source and dynamic time-slot strategy to accelerate transmission and enhance service quality. The proposed model identifies the optimal sources, paths, and time slot lengths required to efficiently transmit workloads to their destinations while minimizing costs. Initially, we introduced a Mixed Integer Non-Linear Programming (MINLP) model and subsequently linearized it within our framework. Given the NP-hard nature of the proposed model, its applicability is limited in large-scale environments. To address this issue, we developed an efficient heuristic algorithm that can derive near-optimal solutions in polynomial time. The simulation results demonstrate the effectiveness of the proposed T-COMS model and the heuristic algorithm in terms of the reduction in cost and transmission time for file transfers between geographically distributed DCs.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"867-881"},"PeriodicalIF":5.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998168","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

Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine Data Sharing 基于双向身份的疫苗数据共享内产品功能再加密

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-19 DOI: 10.1109/TCC.2025.3552740

Jing Wang;Yanwei Zhou;Yasi Zhu;Zhiquan Liu;Bo Yang;Mingwu Zhang

{"title":"Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine Data Sharing","authors":"Jing Wang;Yanwei Zhou;Yasi Zhu;Zhiquan Liu;Bo Yang;Mingwu Zhang","doi":"10.1109/TCC.2025.3552740","DOIUrl":"https://doi.org/10.1109/TCC.2025.3552740","url":null,"abstract":"With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical vaccine trials, where public health outcomes hinge on the analysis of sensitive patient data, the imperative to safeguard privacy has never been more pronounced. Traditional encryption methods, though effective at protecting data, often expose vulnerabilities during decryption and lack the ability to support granular data access and computation. One-way re-encryption schemes further impede the agility of data sharing, which is indispensable for the collaborative efforts of research institutions. To address these limitations, we propose a novel bidirectional re-encryption scheme for inner-product functional encryption (IPFE). Our scheme secures data while allowing computation and sharing in an encrypted state, preserving patient privacy without hindering research. By harnessing inner-product functional encryption, our approach allows authorized researchers to extract valuable insights from encrypted data, significantly enhancing privacy protections. Our scheme’s security is predicated on the <inline-formula><tex-math>$l$</tex-math></inline-formula>-ABDHE (augmented bilinear Diffie-Hellman exponent) assumption, ensuring robustness against chosen plaintext attacks within the standard model. This foundation not only secures the data but also yields compact ciphertext length, minimizing storage demands. We introduce a protocol specifically designed for medical vaccine trials, which leverages our bidirectional IB-IPFRE (Identity-Based Inner-Product Functional Re-Encryption) scheme. This protocol enhances data security, supports collaborative research, and maintains patient privacy. Its application in vaccine trials demonstrates the scheme’s effectiveness in protecting sensitive information while enabling critical research insights.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"617-628"},"PeriodicalIF":5.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230527","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

Dynamic QoS-Driven Framework for Co-Scheduling of Distributed Long-Running Applications on Shared Clusters 共享集群上分布式长时间运行应用协同调度的动态qos驱动框架

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-16 DOI: 10.1109/TCC.2025.3571098

Jianyong Zhu;Hongtao Wang;Pan Su;Yang Wang;Weihua Pan

{"title":"Dynamic QoS-Driven Framework for Co-Scheduling of Distributed Long-Running Applications on Shared Clusters","authors":"Jianyong Zhu;Hongtao Wang;Pan Su;Yang Wang;Weihua Pan","doi":"10.1109/TCC.2025.3571098","DOIUrl":"https://doi.org/10.1109/TCC.2025.3571098","url":null,"abstract":"Cloud service providers typically co-locate various workloads within the same production cluster to improve resource utilization and reduce operational costs. These workloads primarily consist of batch analysis jobs composed of multiple parallel short-running tasks and long-running applications (LRAs) that continuously reside in the system. The adoption of microservice architecture has led to the emergence of distributed LRAs (DLRAs), which enhance deployment flexibility but pose challenges in detecting and investigating QoS violations due to workload variability and performance propagation across microservices. State-of-the-art resource managers are only responsible for resource allocation among applications/jobs and do not prioritize runtime QoS aspects, such as application-level latency. To address this, we introduce Prank, a QoS-driven resource management framework for co-located workloads. Prank incorporates a non-intrusive performance anomaly detection mechanism for DLRAs and proposes a root cause localization algorithm based on PageRank-weighted analysis of performance anomalies. Moreover, it dynamically balances resource allocation between DLRAs and co-located batch jobs on nodes hosting critical microservices, optimizing for both DLRA performance and overall cluster efficiency. Experimental results demonstrate that Prank outperforms state-of-the-art baselines, reducing DLRA tail latency by over 38% while increasing batch job completion time by no more than 21% on average.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"837-853"},"PeriodicalIF":5.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997919","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

Personalized Cloud Gaming: Multi-Objective Optimization for Resource Utilization and Video Encoding 个性化云游戏：资源利用和视频编码的多目标优化

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-16 DOI: 10.1109/TCC.2025.3571095

Jingjing Zhang;Xiaoheng Deng;Jinsong Gui;Xuechen Chen;Shaohua Wan;Geyong Min

{"title":"Personalized Cloud Gaming: Multi-Objective Optimization for Resource Utilization and Video Encoding","authors":"Jingjing Zhang;Xiaoheng Deng;Jinsong Gui;Xuechen Chen;Shaohua Wan;Geyong Min","doi":"10.1109/TCC.2025.3571095","DOIUrl":"https://doi.org/10.1109/TCC.2025.3571095","url":null,"abstract":"Cloud gaming represents a major part of contemporary gaming. To boost the Quality-of-Experience (QoE) of cloud gaming, the integration of Dynamic Adaptive Video Encoding (DAVE) with Multi-access Edge Computing (MEC) has become the natural candidate owing to its flexibility and reliable transmission support for real-time interactions. However, as multiple gamers compete for limited resources to achieve personalized QoE, such as ultra-high video quality and ultra-low latency, how to support efficient edge resource optimization is a fundamental and important problem. Furthermore, determining the optimal game video encoding configuration in real-time poses significant challenges, especially when lacking the information on future video and edge network resources. To address these key issues, we jointly optimize the video encoding as well as computing and communication resource allocation by active mutual adaptation of video coding configurations and physical resources in a Software Defined Networking (SDN)-assisted edge network. This eliminates the performance bottleneck caused by decoupling optimization of coding parameter configuration and physical resource allocation. The SDN-assisted edge network architecture supports efficient on-demand resource management, provides global network information, and meets the stringent time-varying game requests. Due to the significant time scale difference between video chunk and physical resource block, we propose a novel Asynchronous Decision-Making Multi Agent Proximal Policy Optimization algorithm (AD-MAPPO), which can address the credit assignment problem with a single agent. It can also adapt to the highly dynamic cloud gaming environment without prior knowledge and a deterministic environmental model. Extensive experimentation based on real cloud gaming datasets convincingly demonstrates that our approach can significantly enhance the overall QoE of gamers.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"854-866"},"PeriodicalIF":5.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998013","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

Lattice-Based Revocable IBEET Scheme for Mobile Cloud Computing 基于栅格的移动云计算可撤销IBEET方案

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-14 DOI: 10.1109/TCC.2025.3570332

Hongwei Wang;Yongjian Liao;Zhishuo Zhang;Yingjie Dong;Shijie Zhou

{"title":"Lattice-Based Revocable IBEET Scheme for Mobile Cloud Computing","authors":"Hongwei Wang;Yongjian Liao;Zhishuo Zhang;Yingjie Dong;Shijie Zhou","doi":"10.1109/TCC.2025.3570332","DOIUrl":"https://doi.org/10.1109/TCC.2025.3570332","url":null,"abstract":"Identity-based encryption with equality test (IBEET) is a special form of searchable encryption that has broad applications in cloud computing. It enables users to perform equality tests on encrypted data without decryption, thereby achieving secure data search while ensuring data privacy and confidentiality. However, in the context of mobile cloud computing, the susceptibility of mobile devices to loss significantly increases the risk of private key exposure. Existing IBEET schemes struggle to address this issue effectively, limiting their practical applicability. Moreover, with the rapid advancement of quantum computing, the security of traditional cryptographic hardness assumptions faces potential threats. To address these challenges and enhance system efficiency, we proposes the first lattice-based revocable IBEET (RIBEET) scheme, which supports user key revocation. We prove that our scheme satisfies adaptive CCA security under the assumption of DLWE hard problem. Additionally, performance evaluations comparing our scheme with existing ones demonstrate that our scheme offers significant efficiency advantages. Furthermore, we apply the proposed scheme to mobile health services, showcasing its practicality and reliability in mobile cloud computing environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"807-820"},"PeriodicalIF":5.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998285","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

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-14 DOI: 10.1109/TCC.2025.3570093

Behshid Shayesteh;Chunyan Fu;Amin Ebrahimzadeh;Roch H. Glitho

{"title":"Data-Related Parameter Selection for Training Deep Learning Models Predicting Application Performance Degradation in Clouds","authors":"Behshid Shayesteh;Chunyan Fu;Amin Ebrahimzadeh;Roch H. Glitho","doi":"10.1109/TCC.2025.3570093","DOIUrl":"https://doi.org/10.1109/TCC.2025.3570093","url":null,"abstract":"Applications deployed in clouds are susceptible to performance degradation due to diverse underlying causes such as infrastructure faults. To maintain the expected availability of these applications, Machine Learning (ML) models can be used to predict the impending application performance degradations to take preventive measures. However, the prediction accuracy of these ML models, which is a key indicator of their performance, is influenced by several factors, including training data size, data sampling intervals, input window and prediction horizon. To optimize these data-related parameters, in this article, we propose a surrogate-assisted multi-objective optimization algorithm with the objective to maximize prediction model accuracy while minimizing the resources consumed for data collection and storage. We evaluated the proposed algorithm through two use cases focusing on the prediction of Key Performance Indicators (KPIs) for a 5G core network and a web application deployed in two Kubernetes-based cloud testbeds. It is demonstrated that the proposed algorithm can achieve a normalized hypervolume of 99.5% relative to the optimal Pareto front and reduce search time for the optimal solution by 0.6 hours compared to other surrogates and by 3.58 hours compared to using no surrogates.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"794-806"},"PeriodicalIF":5.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998333","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