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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Asynchronous Entanglement Provisioning and Routing for distributed Quantum computing 42

Asynchronous Entanglement Provisioning and Routing for Distr...

引用

42nd ieee international conference on Computer Communications, INFOCOM 2023

作者： Yang, Lan Zhao, Yangming Huang, Liusheng Qiao, Chunming University of Science and Technology of China School of Computer Science and Technology China University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350334142

In distributed Quantum computing (DQC), quantum bits (qubits) used in a quantum circuit may be distributed on multiple Quantum Computers (QCs) connected by a Quantum Data Network (QDN). To perform a quantum gate operation involving two qubits on different QCs, we have to establish an Entanglement Connection (EC) between their host QCs. Existing EC establishment schemes result in a long EC establishment time, and low quantum resource *** this paper, we propose an Asynchronous Entanglement Routing and Provisioning (AEPR) scheme to minimize the task completion time in DQC systems. AEPR has three distinct features: (i). Entanglement Paths (EPs) for a given SD pair are predetermined to eliminate the need for runtime calculation;(ii). Entanglement Links (ELs) are created proactively to reduce the time needed create EL on demand;and (iii). For a given EC request, quantum swapping along an EP is performed by a repeater whenever two adjacent ELs are created, so precious quantum resources at the repeater can be released immediately thereafter for other ELs and ECs. Extensive simulations show that AEPR can save up to 76.05% of the average task completion time in DQC systems compared with the state-of-the-art entanglement routing schemes designed to maximize QDN throughput. © 2023 ieee.

关键词： Quantum entanglement

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BTDFS: B+ Tree-Based distributed File Storing Mechanism for Improving Small File Storage 24

BTDFS: B+ Tree-Based Distributed File Storing Mechanism for ...

引用

24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jingyu Tian, Jiachen Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Systems Engineering National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350319934

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. This paper proposes a novel distributed file storing mechanism called B+ Tree-Based distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. The experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods. © 2022 ieee.

关键词： Digital storage

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Accelerated Flag Qubit Allocation for Fault-Tolerant Quantum Error Correction 15

Accelerated Flag Qubit Allocation for Fault-Tolerant Quantum...

引用

15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cho, Eunyoung Cha, Gyuil Electronics and Telecommunications Research Institute Cloud Computing Core Sw Research Section Future Computing Research Division Artificial Intelligence Research Laboratory Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In quantum computing, achieving error correction comparable to classical systems is crucial for long-term success. This involves measuring and extracting error syndromes to prevent error propagation. However, the constraints of quantum hardware necessitate optimizing these circuits for spatial and temporal efficiency. This paper presents a case study on a flag-based syndrome extraction technique, which aims to maximize parallelism while minimizing resource overhead. To address the scalability of quantum systems and adapt to various hardware constraints, a novel qubit allocation acceleration technique using precalculated unit-cells is proposed. This method enhances both the efficiency and flexibility of quantum compilers, making them more adept at handling quantum error correction challenges. © 2024 ieee.

关键词： Qubits

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Cryptographic Reverse Firewall for Digital Signature in Fog computing 6

Cryptographic Reverse Firewall for Digital Signature in Fog ...

引用

6th international conference on Software Engineering and Computer Science, CSECS 2023

作者： Kang, Burong Lian, Huanhuan Zhou, Xuhua Zhou, Weniun Research Institute of China Telecom Corporation Limited Security Technology Research Division Shanghai China

ISBN: (纸本)9798350306378

Fog computing (FC) is introduced as an extended technique of cloud computing. FC makes it possible to compute straightly on the brim of the network. In FC application systems, authentication is an important security requirement when fog nodes communicate with other fog devices. Digital signature (DS) has been widely used in FC systems to provide the non-repudiation, authenticity along with data integrity. However, some latest research results demonstate that DS schemes may confront the algorithm substitution attack (ASA). That is, DS scheme would be implanted some covert backdoor information in algorithm implementation process by attackers. With the backdoor information attackers can recover the secret key of fog devices' users, which will result in privacy disclosure. To settle this matter, the notion of cryptographic reverse firewall (CRF) is proposed, which can re-randomize the inputs and outputs of the fog devices. Moreover, due to the limited storing and computing abilities of fog devices, the DS algorithms applied to FC systems should be resource-saving. In this paper, we simultaneously focus attention on addressing above two problems in fog computing and construct the CRFs for the Camenisch and Lysyanskaya DS scheme (CL signature) which is suitable to be applied in FC systems because of its high efficiency. © 2023 ieee.

关键词： Fog computing

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CryptoQFL: Quantum Federated Learning on Encrypted Data 4

CryptoQFL: Quantum Federated Learning on Encrypted Data

引用

4th ieee international conference on Quantum computing and Engineering, QCE 2023

作者： Chu, Cheng Jiang, Lei Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798350343236

Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server;(2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation;and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches. © 2023 ieee.

关键词： Data privacy

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A SPB based LB Algorithm in Order to Schedule the Sequence Task in CCS 4

A SPB based LB Algorithm in Order to Schedule the Sequence T...

引用

4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Bindu, G. B. Hima Manjula, L. Tellur, Anil Vincy Devi, V.K. Nasrulaah, Najlaa Muhsin, Omar The Apollo University Department of Cse School of Technology Chittoor India The Oxford College of Engineering Department of Computer Science and Engineering Bangalore India Karnataka Bengaluru India Adi Shankara Institute of Engineering&Technology Department of Computer Applications kalady India National University of Science and Technology Faris Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The increasing pervasiveness of computers in almost every aspect of our everyday lives has led to a commensurate rise in the need for computing resources, including bandwidth, processing power, and storage space. Originally, computer networks were created to address this difficulty, which helped ideas and paradigms like distributed computing, grid computing, and cluster computing to develop. But providing such services calls for advanced methods to effectively handle workloads and quickly attend to customer demands. One of the major problems with cloud computing is load balancing, which is a vital procedure that distributes work among virtual computers to avoid excess or underuse. In order to support the increasing number of users, the algorithms controlling this work assignment need to balance robustness with efficient use of resources. The related costs and hazards increase, especially for larger organisations, which emphasises how crucial it is to evaluate the risks involved in deploying load balancing algorithms in order to achieve desired service levels. Assessing the algorithms, applications, and rules in a virtualized setting is a useful method for risk assessments. With the use of simulation tools, one may practically mimic the intricacies of actual cloud settings and gain insights into possible problems and performance. As a powerful simulation toolset, CloudSim is intended to fully represent and simulate various cloud environments. It makes experimentation and optimisation easier by providing the ability to design and test different load balancing algorithms and cloud broker policies. © 2024 ieee.

关键词： Cluster computing

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A Comparison of Traditional Natural Language Processing Methods for Emotion Recognition Using Quantum computing 2

A Comparison of Traditional Natural Language Processing Meth...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Thota, Srirajarajeswari Dash, Sandeep Kumar National Institute of Technology Department of CSE Mizoram Aizawl India

ISBN: (纸本)9798331505745

Emotion Recognition plays an important role in recognizing human behavior. Human behavior may be represented in text, speech, and facial expressions. In traditional form, natural language processing known as NLP is used to extract features to evaluate emotions. With the evolution of Quantum computing, Quantum Machine learning often called QML is one of the research areas used to evaluate emotions. This paper is designed to compare traditional ML models with QML models to analyze the sentiments and emotions of multimodal data. Here, NLP techniques are used for preprocessing. A hybrid model is built by combining NLP and QML. The preprocessed data are transmitted into quantum circuits for computation, and its results are compared with conventional machine learning models. This evaluation identifies QML as a promising future as it is useful for large and complex data sets with high dimensions. This study highlights the potential of QML in NLP and outlines the challenges such as encoding and hardware limitations. © 2025 ieee.

关键词： Sentiment analysis

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Balancing on the Edge: Fast Service or No Service 23

Balancing on the Edge: Fast Service or No Service

引用

23rd ieee international conference on Communication Technology, ICCT 2023

作者： Peng, Pei Soljanin, Emina Nanjing University of Posts and Telecommunications Nanjing China Rutgers the State University of New Jersey Piscataway United States

ISBN: (纸本)9798350325959

Edge computing operates between the cloud and end-users, and strives to provide computing services at a high rate. Since the computing and storage resources are limeted, directing more resources to some computing jobs will block (and pass to the cloud) the execution of others. We evaluate the system performance with two metrics: job computing time and job blocking probability. Edge nodes often operate in highly unpredictable environments, and replicating job execution when resources allow improves the job mean execution time. We show that the job computing time increases with the number of groups, but the job blocking probability does not. That is, there is a tradeoff between job computing time and blocking probability. This paper adopts the average system time as a single system's performance indicator to evaluate the tradeoff. We conclude that the optimal number of groups that maximizes the system service rate changes with arrival rate and cloud time. © 2023 ieee.

关键词： blocking system distributed computing Edge computing resource allocation

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Research on Parallel computing Method of Kalman Filter Based on CUDA 5

Research on Parallel Computing Method of Kalman Filter Based...

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5th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Sheng, Panlong He, Pengju Xu, Yimei Zhao, Xiangmei Xi'an Eurasia University School of Information Engineering Xi'an China

ISBN: (纸本)9798350366174

The Kalman filter is an important tool for state estimation of linear dynamic systems and is widely used in signal processing, control systems, navigation and other fields. The computational efficiency of the traditional Kalman filter is limited when processing large-scale data. This article proposes a Kalman filter parallel computing method based on CUDA (Compute Unified Device Architecture), which improves processing speed by executing filter steps in parallel on the GPU. Experimental results show that compared with traditional CPU implementation, the Kalman filter implemented by CUDA shows significant acceleration effect on large data sets. © 2024 ieee.

关键词： Kalman filters

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A Survey of Machine Learning Technique for Topic Modeling and Word Embedding 10

A Survey of Machine Learning Technique for Topic Modeling an...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Saranya, M. Amutha, B. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

'Word embedding' is a key phrase in natural language processing that describes how words are expressed as real-valued vectors for text analysis. A breakthrough in natural language processing (NLP) has improved computers' ability to understand textual content. It is thought to be among the most significant developments in deep learning for addressing challenging problems in natural language processing. Since words and texts are represented as numeric vectors, this technique permits related words to have comparable vector representations. The gathered features are integrated into a machine learning model to handle text data while preserving syntactic and semantic information. Once translated, these data are used by natural language processing (NLP) algorithms, which can process textual material and absorb these learned representations fast. This paper focuses on word embedding methods used to LDA, LSA, PLSA, Bag of Words, TF-IDF, Word2Vec, GloVe, and BERT, among other natural language processing applications. © 2024 ieee.

关键词： Embeddings

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