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Publicly Verifiable distributed Computation for MEC Setting

IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2025年第7期36卷 1416-1430页

作者： Wang, Qiang Li, Zhicheng Zhou, Fucai Xu, Jian Zhang, Changsheng Northeastern Univ Software Coll Shenyang 110013 Peoples R China

With the rapid expansion of the Internet of Things (IoT), the shift from cloud computing to Mobile Edge computing (MEC) has become necessary to address the low-latency requirements of real-time applications. Verifiable computation (VC) enables resource-limited clients to outsource their computation-intensive tasks to a powerful cloud while ensuring the correctness of the computation result. However, traditional VC schemes, originally designed for cloud computing, face challenges when applied to MEC environments, such as scalability issues, robustness, and efficiency concerns. To this end, we propose a verifiable distributed computation scheme for MEC, where computation tasks are distributed between a cloud server cluster (consisting of $n$n servers) and an edge server. The cloud handles most of the computation through parallel sub-tasks, while the edge server verifies intermediate results and performs minimal computation to recover the final outcome. Our scheme guarantees that the result can be recovered if at least $t$t servers, out of a total of $n$n servers in the cloud server cluster, perform their computations honestly. By leveraging batch verification and matrix-optimized polynomial evaluations, our scheme significantly enhances scalability, fault tolerance, and efficiency. The extensive analysis and simulations demonstrate that our proposed scheme is more feasible than existing solutions.

关键词： Servers Cloud computing Internet of Things Real-time systems Autonomous vehicles Low latency communication Computational efficiency Robustness Multi-access edge computing Computer architecture distributed computing mobile edge computing verifiable computation

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Release 2.0-NEMSIM-RT: A real-time distributed spiking neural network simulator

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SOFTWAREX 2024年 26卷

作者： Quintana, Fernando M. de la Torre, Juan C. Barcena-Gonzalez, Guillermo Guerrero-Lebrero, Maria P. Guerrero, Elisa Univ Cadiz Comp Sci Dept Intelligent Syst Res Grp Cadiz Spain

NESIM-RT is a specialized tool designed for simulating neuromorphic systems. In this new release we extend its capabilities to include state-of-the art models like the AdexLIF and Izhikevich, and to incorporate dynamic synaptic mechanisms such as Spike-Timing Dependent Plasticity (STDP). With these new features, researchers can now observe in real -time how different parameters influence these models and learning rules, thereby gaining deeper insights into neuronal function and network dynamics.

关键词： Spiking neural network Neuromorphic systems distributed computing Synaptic plasticity

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distributed Matrix Computations With Low-Weight Encodings

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2023年 4卷 363-378页

作者： Das, Anindya Bijoy Ramamoorthy, Aditya Love, David J. Brinton, Christopher G. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such stragglers is to incorporate Reed-Solomon based MDS (maximum distance separable) codes into the framework;this can achieve resilience against an optimal number of stragglers. However, these codes assign dense linear combinations of submatrices to the worker nodes. When the input matrices are sparse, these approaches increase the number of non-zero entries in the encoded matrices, which in turn adversely affects the worker computation time. In this work, we develop a distributed matrix computation approach where the assigned encoded submatrices are random linear combinations of a small number of submatrices. In addition to being well suited for sparse input matrices, our approach continues to have the optimal straggler resilience in a certain range of problem parameters. Moreover, compared to recent sparse matrix computation approaches, the search for a "good" set of random coefficients to promote numerical stability in our method is much more computationally efficient. We show that our approach can efficiently utilize partial computations done by slower worker nodes in a heterogeneous system which can enhance the overall computation speed. Numerical experiments conducted through Amazon Web Services (AWS) demonstrate up to 30% reduction in per worker node computation time and 100x faster encoding compared to the available methods.

关键词： distributed computing MDS codes stragglers condition number sparsity

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SimE4KG: distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

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INTERNATIONAL JOURNAL OF SEMANTIC computing 2023年第2期17卷 199-221页

作者： Draschner, Carsten Felix Jabeen, Hajira Lehmann, Jens Univ Bonn Machine Learning & Articial Intelligence Lab Bonn Germany GESIS Leibniz Inst Social Sci D-50667 Cologne Germany Amazon Alexa AI Dresden Germany

In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).

关键词： Semantic similarity knowledge graphs distributed computing explainable artificial intelligence scalable semantic processing RDF Apache Spark machine learning

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Principal Component Analysis Scheme Based on Homomorphic Encryption in a distributed Environment 21st

Principal Component Analysis Scheme Based on Homomorphic Enc...

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21st International Conference on Web Information Systems and Applications (WISA)

作者： Wang, Dong Ma, Ming Cheng Liu, Lingli Du, Xiongxiong Li, Xiaoruo Zhu, Bingnan Henan Univ Henan Int Joint Lab Intelligent Network Theory & Kaifeng 475000 Peoples R China Henan Univ Sch Software Kaifeng 475000 Peoples R China

ISBN: (纸本)9789819777068;9789819777075

Principal component analysis (PCA) is a widely used technique in the field of machine learning and one of the main dimensionality reduction methods. PCA can convert high-dimensional data into lower-dimensional representations, thereby helping to extract important information from data, and is an effective tool for data analysis and pattern recognition. In this paper, we propose a scheme for executing PCA on encrypted data based on homomorphic encryption in a distributed environment. In a distributed setting, computing nodes do not need to perform matrix operations such as matrix multiplication or addition, which reduces the computational burden on the nodes. To ensure the security of the data, we employ the CKKS homomorphic encryption scheme, which allows for approximate calculations on real numbers, meeting the requirements of machine learning. Additionally, we horizontally partition the data to facilitate its application in distributed computing. Furthermore, we optimize the detailed computation of HPCA (Homomorphic PCA), yielding promising results on various datasets.

关键词： Homomorphic Encryption distributed computing Principal Component Analysis (PCA) CKKS scheme Goldschmidt's Algorithm

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Pseudospheres: combinatorics, topology and distributed systems

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Journal of Applied and Computational Topology 2024年第4期8卷 1023-1052页

作者： Alberto, Luis Instituto de Matematicas Universidad Nacional Autonoma de Mexico Av. Universidad Mexico City 04510 Mexico

Pseudospheres are simplicial complexes defined in the late 1990s to model some aspects of distributed systems. Since then, combinatorial properties of pseudospheres combined with topological properties have been very useful to derive distributed computability results. The goal of this paper is to study pseudospheres in more depth as mathematical objects and to give an overview of the properties that have been used in distributed computing. In this work we focus in combinatorial and topological aspects of pseudospheres. While doing so, the paper shows that these structures can be viewed from different perspectives, in addition to models of distributed computing. We show that the properties of pseudospheres that have been proved in distributed computing, as well as new ones, can be derived using combinatorial topology techniques and other combinatorial techniques taken from matroids and partial orders. A subclass of pseudospheres is related to universal bundles, and the Borsuk–Ulam theorem can be extended to apply to them. © The Author(s) 2024.

关键词： 05B35 06A06 55U10 68M14 Borsuk–Ulam distributed computing Matroids Posets

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Towards a Scalable and Efficient PGAS-Based distributed OpenMP 1

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20th International Workshop on OpenMP (IWOMP)

作者： Shan, Baodi Araya-Polo, Mauricio Chapman, Barbara SUNY Stony Brook Stony Brook NY 11794 USA TotalEnergies EP Res & Technol US LLC Houston TX 77002 USA

ISBN: (数字)9783031725678

ISBN: (纸本)9783031725661;9783031725678

MPI+X has been the de facto standard for distributed memory parallel programming. It is widely used primarily as an explicit two-sided communication model, which often leads to complex and error-prone code. Alternatively, PGAS model utilizes efficient one-sided communication and more intuitive communication primitives. In this paper, we present a novel approach that integrates PGAS concepts into the OpenMP programming model, leveraging the LLVM compiler infrastructure and the GASNet-EX communication library. Our model addresses the complexity associated with traditional MPI+OpenMP programming models while ensuring excellent performance and scalability. We evaluate our approach using a set of micro-benchmarks and application kernels on two distinct platforms: Ookami from Stony Brook University and NERSC Perlmutter. The results demonstrate that DiOMP achieves superior bandwidth and lower latency compared to MPI+OpenMP, up to 25% higher bandwidth and down to 45% on latency. DiOMP offers a promising alternative to the traditionalMPI+OpenMP hybrid programmingmodel, towards providing a more productive and efficient way to develop high-performance parallel applications for distributed memory systems.

关键词： PGAS MPI OpenMP distributed computing

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A Web-based Platform for Publication and distributed Execution of computing Applications 14

A Web-based Platform for Publication and Distributed Executi...

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IEEE 14th International Symposium on Parallel and distributed computing (ISPDC)

作者： Sukhoroslov, Oleg Volkov, Sergey Afanasiev, Alexander Russian Acad Sci Kharkevich Inst Inst Informat Transmiss Problems Ctr Distributed Comp Moscow Russia

ISBN: (纸本)9781467371483

Researchers increasingly rely on using web-based systems for accessing and running scientific applications across distributed computing resources. However existing systems lack a number of important features, such as publication and sharing of scientific applications as online services, decoupling of applications from computing resources and providing remote programmatic access. This paper presents Everest, a web-based platform for researchers supporting publication, execution and composition of applications running across distributed computing resources. Everest addresses the described challenges by relying on modern web technologies and cloud computing models. It follows the Platform as a Service (PaaS) cloud delivery model by providing all its functionality via remote web and programming interfaces. Any application added to Everest is automatically published both as a user-facing web form and a web service. Another distinct feature of Everest is the ability to attach external computing resources by any user and flexibly use these resources for running applications. The paper provides an overview of the platform's architecture and its main components, describes recent developments, presents results of experimental evaluation of the platform and discusses remaining challenges.

关键词： distributed computing web-based platform web services REST API software as a service platform as a service resource integration application composition

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Special Section on distributed Edge Learning in Wireless Networks

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2023年 4卷 2729-2732页

作者： Gong, Xiaowen Huang, Kaibin Fischione, Carlo Chen, Mingzhe Zhang, Jun Choi, Wan Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China KTH Royal Inst Technol Div Networks & Syst Engn S-11428 Stockholm Sweden Univ Miami Dept Elect & Comp Engn Miami FL 33136 USA Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea

distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance.

关键词： Special issues and sections Edge computing distributed computing Wireless networks Learning systems Machine learning

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Signaling Storm Mitigation by Geographically distributed C-plane NF Placement and Routing 10

Signaling Storm Mitigation by Geographically Distributed C-p...

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10th International Conference on Network Softwarization (NetSoft)

作者： Kurata, Masayuki Ikami, Akio Itahara, Sohei Suzuki, Masaki KDDI Res Inc Fujimino Japan

ISBN: (纸本)9798350369588;9798350369595

In mobile networks, network performance can be severely degraded by a signaling storm, a phenomenon characterized by control plane (C-plane) congestion due to excessive signaling messages. This paper identifies congestion of inter-site links and overload of a specific NF instance as factors leading to a signaling storm. To address these problems, we propose a combined placement and routing algorithm approach. Firstly, our joint placement and routing algorithms perform geographically distributed deployment of C-plane NF instances according to the fluctuating mobility patterns of each user equipment (UE). Secondly, our routing algorithm identifies an appropriate C-plane NF instance to manage each UE, considering both the utilization rate and the number of contexts possessed by each NF instance. Extensive simulation evaluations demonstrate that our proposed methods significantly reduce inter-site signaling messages compared to existing placement scenarios. Furthermore, the dispersion of C-plane NF instances' utilization rate is reduced, enhancing network performance and efficiency.

关键词： Signaling storm Mobile computing distributed computing Placement algorithm Routing algorithm

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