IEEE Transactions on Cloud Computing最新文献_第9页

A Cloud-Edge Collaboration Framework for Generating Process Digital Twin 生成流程数字双胞胎的云端协作框架

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-02-06 DOI: 10.1109/TCC.2024.3362989

Bingqing Shen;Han Yu;Pan Hu;Hongming Cai;Jingzhi Guo;Boyi Xu;Lihong Jiang

{"title":"A Cloud-Edge Collaboration Framework for Generating Process Digital Twin","authors":"Bingqing Shen;Han Yu;Pan Hu;Hongming Cai;Jingzhi Guo;Boyi Xu;Lihong Jiang","doi":"10.1109/TCC.2024.3362989","DOIUrl":"10.1109/TCC.2024.3362989","url":null,"abstract":"Tracking the process of remote task execution is critical to timely process analysis by collecting the evidence of correct execution or failure, which generates a process digital twin (DT) for remote supervision. Generally, it will encounter the challenge of constrained communication, high overhead, and high traceability demand, leading to the efficient remote process tracking issue. Existing approaches can address the issue by monitoring or simulating remote task execution. Nevertheless, they do not provide a cost-effective solution, especially when unexpected situation occurs. Thus, we proposed a new cloud-edge collaboration framework for process DT generation. It addresses the efficient remote process tracking issue with a real-virtual collaborative process tracking (RVCPT) approach. The approach contains three patterns of real-virtual collaboration for tracking the entire process of task execution with a coevolution pattern, identifying unexpected situations with a discrimination pattern, and generating a process DT with a real-virtual fusion pattern. This approach can minimize tracking overhead, and meanwhile maintains high traceability, which maximizes the overall cost-effectiveness. With prototype development, case study and experimental evaluation show the applicability and performance advantage of the new cloud-edge collaboration framework in remote supervision.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"388-404"},"PeriodicalIF":6.5,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956141","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

Cloud-Assisted Laconic Private Set Intersection Cardinality 云辅助 Laconic 私有集交集卡性

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-02-05 DOI: 10.1109/TCC.2024.3361882

Axin Wu;Xiangjun Xin;Jianhao Zhu;Wei Liu;Chang Song;Guoteng Li

{"title":"Cloud-Assisted Laconic Private Set Intersection Cardinality","authors":"Axin Wu;Xiangjun Xin;Jianhao Zhu;Wei Liu;Chang Song;Guoteng Li","doi":"10.1109/TCC.2024.3361882","DOIUrl":"10.1109/TCC.2024.3361882","url":null,"abstract":"Laconic Private Set Intersection (LPSI) is a type of PSI protocols characterized by the requirement of only two-round interactions and by having a reused message in the first round that is independent of the set size. Recently, Aranha et al. (CCS’2022) proposed a LPSI protocol that utilizes the pairing-based accumulator. However, this protocol heavily relies on time-consuming bilinear pairing operations, which can potentially cause a bottleneck. Furthermore, in certain scenarios like contact tracing, it is sufficient to only reveal the intersection cardinality. To tackle this problem and expand on its functionalities, we introduce a cloud-assisted two-party LPSI cardinality (TLPSI-CA) that inherits the properties of LPSI. Interestingly, the cloud-assisted TLPSI-CA eliminates the direct interaction between the sender and receiver, enabling the sender's message to be reused across any number of protocol executions. Besides, we further extend it to the multi-party scenario, which also possesses laconic properties. Then, we prove the two protocols’ security in achieving the defined ideal functionalities. Finally, we evaluate the performance of both protocols and find that TLPSI-CA successfully reduces the local computation costs for participants. Additionally, the multi-party protocol performs similarly to TLPSI-CA, with the exception of the higher communication costs incurred by the receiver.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"295-305"},"PeriodicalIF":6.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951363","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

Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network 边缘云协作无人机目标检测：使用模糊神经网络的边缘嵌入式轻量级算法设计和任务卸载

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-02-05 DOI: 10.1109/TCC.2024.3361858

Yazhou Yuan;Shicong Gao;Ziteng Zhang;Wenye Wang;Zhezhuang Xu;Zhixin Liu

{"title":"Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network","authors":"Yazhou Yuan;Shicong Gao;Ziteng Zhang;Wenye Wang;Zhezhuang Xu;Zhixin Liu","doi":"10.1109/TCC.2024.3361858","DOIUrl":"10.1109/TCC.2024.3361858","url":null,"abstract":"With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (\u0000<inline-formula><tex-math>${{text{E}}^{2}}text{L}$</tex-math></inline-formula>\u0000) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"306-318"},"PeriodicalIF":6.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951290","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

Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems 整合贝叶斯优化和机器学习，实现云系统的最优配置

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-02-01 DOI: 10.1109/TCC.2024.3361070

Bruno Guindani;Danilo Ardagna;Alessandra Guglielmi;Roberto Rocco;Gianluca Palermo

引用次数: 0

DRL-Based Contract Incentive for Wireless-Powered and UAV-Assisted Backscattering MEC System 基于 DRL 的无线供电和无人机辅助反向散射 MEC 系统合同激励机制

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-31 DOI: 10.1109/TCC.2024.3360443

Che Chen;Shimin Gong;Wenjie Zhang;Yifeng Zheng;Yeo Chai Kiat

{"title":"DRL-Based Contract Incentive for Wireless-Powered and UAV-Assisted Backscattering MEC System","authors":"Che Chen;Shimin Gong;Wenjie Zhang;Yifeng Zheng;Yeo Chai Kiat","doi":"10.1109/TCC.2024.3360443","DOIUrl":"10.1109/TCC.2024.3360443","url":null,"abstract":"Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this article, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs’ energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users’ random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. First, we use deep Q-network (DQN) algorithm to update the HSs’ offloading decisions according to the changing network environment. Second, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"264-276"},"PeriodicalIF":6.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951383","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

Efficient Verifiable Cloud-Assisted PSI Cardinality for Privacy-Preserving Contact Tracing 用于保护隐私的联系人追踪的高效可验证云辅助 PSI Cardinality

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-30 DOI: 10.1109/TCC.2024.3360098

Yafeng Chen;Axin Wu;Yuer Yang;Xiangjun Xin;Chang Song

{"title":"Efficient Verifiable Cloud-Assisted PSI Cardinality for Privacy-Preserving Contact Tracing","authors":"Yafeng Chen;Axin Wu;Yuer Yang;Xiangjun Xin;Chang Song","doi":"10.1109/TCC.2024.3360098","DOIUrl":"10.1109/TCC.2024.3360098","url":null,"abstract":"Private set intersection cardinality (PSI-CA) allows two parties to learn the size of the intersection between two private sets without revealing other additional information, which is a promising technique to solve privacy concerns in contact tracing. Efficient PSI protocols typically use oblivious transfer, involving multiple rounds of interaction and leading to heavy local computation overheads and protocol delays, especially when interacting with many receivers. Cloud-assisted PSI-CA is a better solution as it relieves participants’ burdens of computation and communication. However, cloud servers may return incorrect or incomplete results for some reason, leading to an incorrectness issue. At present, to our knowledge, existing cloud-assisted PSI-CA protocols cannot address such a concern. To address this, we propose two specific verifiable cloud-assisted PSI-CA protocols: one based on a two-server protocol and the other on a single-server protocol. Further, we employ Cuckoo hashing to optimize these two protocols, enabling the receiver's computational costs independent of the size of the sender's set. We also prove the security of the protocols and implement them. Finally, we analyze and discuss their performance demonstrating that the single-server verifiable PSI-CA protocol does not introduce significant computation or communication costs while adding functionalities.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"251-263"},"PeriodicalIF":6.5,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951366","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

Root Cause Analysis for Cloud-Native Applications 云原生应用的根本原因分析

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-29 DOI: 10.1109/TCC.2024.3358823

Bartosz Żurkowski;Krzysztof Zieliński

{"title":"Root Cause Analysis for Cloud-Native Applications","authors":"Bartosz Żurkowski;Krzysztof Zieliński","doi":"10.1109/TCC.2024.3358823","DOIUrl":"10.1109/TCC.2024.3358823","url":null,"abstract":"Root cause analysis (RCA) is a critical component in maintaining the reliability and performance of modern cloud applications. However, due to the inherent complexity of cloud environments, traditional RCA techniques become insufficient in supporting system administrators in daily incident response routines. This article presents an RCA solution specifically designed for cloud applications, capable of pinpointing failure root causes and recreating complete fault trajectories from the root cause to the effect. The novelty of our approach lies in approximating causal symptom dependencies by synergizing several symptom correlation methods that assess symptoms in terms of structural, semantic, and temporal aspects. The solution integrates statistical methods with system structure and behavior mining, offering a more comprehensive analysis than existing techniques. Based on these concepts, in this work, we provide definitions and construction algorithms for RCA model structures used in the inference, propose a symptom correlation framework encompassing essential elements of symptom data analysis, and provide a detailed description of the elaborated root cause identification process. Functional evaluation on a live microservice-based system demonstrates the effectiveness of our approach in identifying root causes of complex failures across multiple cloud layers.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"232-250"},"PeriodicalIF":6.5,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951361","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

Alleviating Congestion via Switch Design for Fair Buffer Allocation in Datacenters 通过交换机设计缓解拥堵，实现数据中心缓冲区的公平分配

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-23 DOI: 10.1109/TCC.2024.3357595

Ahmed M. Abdelmoniem;Brahim Bensaou

{"title":"Alleviating Congestion via Switch Design for Fair Buffer Allocation in Datacenters","authors":"Ahmed M. Abdelmoniem;Brahim Bensaou","doi":"10.1109/TCC.2024.3357595","DOIUrl":"10.1109/TCC.2024.3357595","url":null,"abstract":"In data-centers, the composite origin and bursty nature of traffic, the small bandwidth-delay product and the tiny switch buffers lead to unusual congestion patterns that are not handled well by traditional end-to-end congestion control mechanisms such as those deployed in TCP. Existing works address the problem by modifying TCP to adapt it to the idiosyncrasies of data-centers. While this is feasible in private environments, it remains almost impossible to achieve practically in public multi-tenant clouds where a multitude of operating systems and thus congestion control protocols co-exist. In this work, we design a simple switch-based active queue management scheme to deal with such congestion issues adequately. Our approach requires no modification to TCP which enables seamless deployment in public data-centers via switch firmware updates. We present a simple analysis to show the stability and effectiveness of our approach, then discuss the real implementations in software and hardware on the NetFPGA platform. Numerical results from ns-2 simulation and experimental results from a small testbed cluster demonstrate the effectiveness of our approach in achieving high overall throughput, good fairness, smaller flow completion times (FCT) for short-lived flows, and a significant reduction in the tail of the FCT distribution.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"219-231"},"PeriodicalIF":6.5,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10412648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951562","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}

引用次数: 0

Fault Tolerance Oriented SFC Optimization in SDN/NFV-Enabled Cloud Environment Based on Deep Reinforcement Learning 基于深度强化学习的 SDN/NFV 云环境中面向容错的 SFC 优化

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-23 DOI: 10.1109/TCC.2024.3357061

Jing Chen;Jia Chen;Kuo Guo;Renkun Hu;Tao Zou;Jun Zhu;Hongke Zhang;Jingjing Liu

{"title":"Fault Tolerance Oriented SFC Optimization in SDN/NFV-Enabled Cloud Environment Based on Deep Reinforcement Learning","authors":"Jing Chen;Jia Chen;Kuo Guo;Renkun Hu;Tao Zou;Jun Zhu;Hongke Zhang;Jingjing Liu","doi":"10.1109/TCC.2024.3357061","DOIUrl":"10.1109/TCC.2024.3357061","url":null,"abstract":"In software defined network/network function virtualization (SDN/NFV)-enabled cloud environment, cloud services can be implemented as service function chains (SFCs), which consist of a series of ordered virtual network functions. However, due to fluctuations of cloud traffic and without knowledge of cloud computing network configuration, designing SFC optimization approach to obtain flexible cloud services in dynamic cloud environment is a pivotal challenge. In this paper, we propose a fault tolerance oriented SFC optimization approach based on deep reinforcement learning. We model fault tolerance oriented SFC elastic optimization problem as a Markov decision process, in which the reward is modeled as a weighted function, including minimizing energy consumption and migration cost, maximizing revenue benefit and load balancing. Then, taking binary integer programming model as constraints of quality of cloud services, we design optimization approaches for single-agent double deep Q-network (SADDQN) and multi-agent DDQN (MADDQN). Among them, MADDQN decentralizes training tasks from control plane to data plane to reduce the probability of single point of failure for the centralized controller. Experimental results show that the designed approaches have better performance. MADDQN can almost reach the upper bound of theoretical solution obtained by assuming a prior knowledge of the dynamics of cloud traffic.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"200-218"},"PeriodicalIF":6.5,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956357","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

Reversible Data Hiding in Shared Images With Separate Cover Image Reconstruction and Secret Extraction 利用独立的封面图像重构和秘密提取在共享图像中进行可逆数据隐藏

IF 6.5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2024-01-09 DOI: 10.1109/TCC.2024.3351143

Lizhi Xiong;Xiao Han;Ching-Nung Yang;Yun-Qing Shi

{"title":"Reversible Data Hiding in Shared Images With Separate Cover Image Reconstruction and Secret Extraction","authors":"Lizhi Xiong;Xiao Han;Ching-Nung Yang;Yun-Qing Shi","doi":"10.1109/TCC.2024.3351143","DOIUrl":"10.1109/TCC.2024.3351143","url":null,"abstract":"Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using \u0000<italic>k</i>\u0000-out-of-\u0000<italic>n</i>\u0000 marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 1","pages":"186-199"},"PeriodicalIF":6.5,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951163","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