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共 356 条记录，以下是21-30 订阅

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A Survey of Mobile Edge computing for the Metaverse: Architectures, Applications, and Challenges

A Survey of Mobile Edge Computing for the Metaverse: Archite...

引用

international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Yitong Wang Jun Zhao School of Computer Science and Engineering Nanyang Technological University Singapore

Metaverse is an emerging virtual universe where humans can have real-time interactions and solid social links like in the physical world, and it opens up a new era of Internet and interactions. In Metaverse, an immersive and photorealistic environment promotes social activities, including education, meetings, and shopping of digital avatars based on critical technologies, including 3D rendering, extended reality, digital twins, artificial intelligence, and Blockchain. However, the limitations of computation, storage, and energy resources restrict the development of Metaverse, and a series of system issues (e.g., latency, security, and battery-life) continue to arise. As a result, how to find corresponding measurements to mitigate unsatisfactory influences becomes the focus. Mobile edge computing (MEC) as a distributed computing paradigm offloads computation-intensive tasks to the edge of the network. It brings the resources as close as possible to the end devices, addressing the shortcomings mentioned above. In this paper, we propose a comprehensive survey of the MEC-based Metaverse. Particular emphasis is given to the technologies convergence, architectures, and application scenarios, e.g., BoundlessXR and CloudXR. Significantly, we introduce the potential future directions for developing Metaverse systems.

关键词： Multi-access edge computing three-dimensional displays Metaverse Computer architecture Solids Rendering (computer graphics) Sparks

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the Influence-Maximizing Node Selection Algorithm Based on Local Isolated Centrality in Large-scale networks Implemented by distributed Graph computing

The Influence-Maximizing Node Selection Algorithm Based on L...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Piaoyang Chen Rong Lei Mingsen Deng School of Information Guizhou University of Finance and Economics Guiyang China

Identifying influence-maximizing (IM) nodes is an effective strategy for designing efficient communication networks or contain the spreading of information for rumors and epidemics with limited budgets. With the growing of the sizes of the social networks, finding solutions to the problem of intfuence − maximizing vertices becomes more and more challenging. Tra-ditionally, the number of users finally affected was the focus. However, the information diffusion speed was becoming a bottle-neck of IM problems. In this paper, we proposed a locality-aware vertex selection strategy based on local isolation centrality (LIC), while the information diffusion experiments used the Independent Cascade (IC) model. the computing scheme was implemented by a distributed graph computing framework. the superiority of the LIC algorithm was confirmed by evaluated on three real-world datasets. It was indistinguishable from algorithms such as K-shell, H-index, degree centrality (DC), local centrality (LC), and VoteRank at the diffusion cascade scale. But it showed better superiority in diffusion speed. More importantly, by comparing LIC with the VoteRank algorithm, which was the closest algorithm on diffusion speed to that of LIC, the huge advantage of the LIC algorithm in terms of time cost was found. We believe that the locality-aware LIC algorithm was more suitable for the influence-maximization problem in very large-scale social networks.

关键词： Epidemics Costs Social networking (online) Computational modeling Communication networks Integrated circuit modeling

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Efficient Transparent Access to 5G Edge Services

Efficient Transparent Access to 5G Edge Services

引用

IEEE conference on Network Softwarization (NetSoft)

作者： Josef Hammer Hermann Hellwagner Institute of Information Technology Alpen-Adria-Universit&#x00E4 t Klagenfurt Austria

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

Multi-access Edge computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. In [1] we argued that edge computing should be transparent to clients and introduced a solution to that end. this paper presents how to efficiently implement such a transparent approach, leveraging Software-Defined networking. For high performance and scalability, our architecture focuses on three aspects: (i) a modular architecture that can easily be distributed onto multiple switches/controllers, (ii) multiple filter stages to avoid screening traffic not intended for the edge, and (iii) several strategies to keep the number of flows low to make the best use of the precious flow table memory in hardware switches. A performance evaluation is shown, with results from a real edge/fog testbed.

关键词： Performance evaluation Multi-access edge computing 5G mobile communication Scalability conferences Computer architecture Switches

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yusen Xu Sijie Shen Mingcong Han Rong Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Shanghai China

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by $1.5\times$ to $3.9\times$ and reduces end-to-end latency by more than $20\times$ compared to Apache Kafka.

关键词： Data centers Publishing Social networking (online) Scalability Semantics Systems architecture Low latency communication

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Impact of Hardware Impairments on the Performance of Millimeter-Wave Massive MU-MIMO Systems with distributed Antennas 8

Impact of Hardware Impairments on the Performance of Millime...

引用

8th international conference on Communications and networking, ComNet2020

作者： Belaoura, Widad Ghanem, Khalida Shakir, Mohummed Zeeshan Qaraqe, Khalid Department of Electronics Algiers Algeria Algiers Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland United Kingdom Texas AM University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9781728153209

the impact of hardware impairments play an crucial role in any practical communication systems, yet they are generally omitted when investigating the performance of distributed massive multiple input multiple output (MIMO) systems. In particularly, for millimeter-Wave (mm-Wave) bands, no study has been addressed yet. To overcome this limitation, in this paper a mm-Wave distributed massive multi-user (MU) MIMO system based-hybrid beamforming structure, in which the residual hardware impairments have been incorporated in the transmitter processing, is presented. At each base station (BS), a zero-forcing (ZF) baseband processing is designed over the effective channel to efficiently remove the multi-user (MU) interference. the results show that, relative to the co-located mm-Wave massive MIMO with the same antenna configurations, mm-Wave distributed MIMO achieves a significantly higher performance while reducing the impingement of hardware practical defects. © 2020 IEEE.

关键词： Millimeter waves

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HeAT - a distributed and GPU-accelerated Tensor Framework for Data Analytics 8

HeAT - a Distributed and GPU-accelerated Tensor Framework fo...

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8th IEEE international conference on Big Data (Big Data)

作者： Goetz, Markus Debus, Charlotte Coquelin, Daniel Krajsek, Kai Comito, Claudia Knechtges, Philipp Hagemeier, Bjorn Tarnawa, Michael Hanselmann, Simon Siggel, Martin Basermann, Achim Streit, Achim German Aerosp Ctr Inst Software Technol SC Cologne Germany Forschungszentrum Julich Inst Rio & Geosci Agrosphere IBG 3 Julich Germany Forschungszentrum Julich FZJ Julich Supercomp Ctr JSC Julich Germany Karlsruhe Inst Technol KIT Steinbuch Ctr Comp SCC Karlsruhe Germany

ISBN: (纸本)9781728162515

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce IleAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly I owering the bartier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.

关键词： IleAT Tensor Framework High-performance computing PyTorch NumPy Message Passing Interface CPU Rig Data Analytics Machine Learning Dask Model Parallelism Parallel Application Frameworks

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Intelligent Resource Allocation in Dynamic Fog computing Environments 8

Intelligent Resource Allocation in Dynamic Fog Computing Env...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： SMeddi, Amina Jaafar, Wael Elbiaze, Halima Ajib, Wessam Univ Quebec Montreal Montreal PQ Canada Carleton Univ Ottawa ON Canada

ISBN: (纸本)9781728148328

Fog computing emerged as a new paradigm that pushes cloud applications to the network edge. the fog infrastructure contains mainly distributed and heterogeneous fog nodes that are characterized by their complex distribution, high mobility and sporadic resources availability. this dynamic fog nodes behavior triggers new challenges in the resource management process, such as resources coordination for continuous quality-of-service satisfaction. In this paper, we propose a smart online resource allocation approach adapted for dynamic fog computing environments, aiming at maximizing the number of satisfied user requests within a predefined delay threshold. We model the fog computing environment as a Markov discrete process, where dynamic fog node behavior / mobility and resources availability are considered. then, we present our smart deep-reinforcement-learning resource allocation algorithm. Considering real-world mobility data sets, the near-optimal performance of the proposed solution is illustrated through simulations, and its superiority over heuristic state-of-the-art approaches is exposed.

关键词： Fog computing resource allocation deep reinforcement learning

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Resource provisioning for highly reliable and ultra-responsive edge applications 8

Resource provisioning for highly reliable and ultra-responsi...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： Toka, Laszlo Haja, David Korosi, Attila Sonkoly, Balazs MTA BME Network Softwarizat Res Grp Budapest Hungary MTA BME Informat Syst Res Grp Budapest Hungary Budapest Univ Technol & Econ Budapest Hungary

ISBN: (纸本)9781728148328

Edge and fog computing are emerging concepts extending traditional cloud computing by deploying compute resources closer to the users. this approach, closely integrated with carrier-networks, enables several future services, such as tactile internet, 5G and beyond telco services, and extended reality applications. the emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed remote nodes, and the sheer scale altogether call for a powerful provisioning platform that offers the applications the best out of the underlying infrastructure. In this paper we investigate the resource provisioning problem in the edge infrastructure with the consideration of probable failures. Our goal is to support high reliability of services with the minimum amount of edge resources reserved to provide the necessary redundancy in the system. We design a resource provisioning algorithm, which takes into account network latency when pinpointing backup placeholders for virtual functions of edge applications. We implement the proposed solution in a simulation environment and show the efficient resource utilization results achieved by our fast heuristic algorithm.

关键词： edge computing high reliability ultra-low latency

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Towards making big data applications network-aware in edge-cloud systems 8

Towards making big data applications network-aware in edge-c...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： Haja, David Vass, Balazs Toka, Laszlo MTA BME Network Softwarizat Res Grp Budapest Hungary Budapest Univ Technol & Econ Budapest Hungary MTA BME Informat Syst Res Grp Budapest Hungary

ISBN: (纸本)9781728148328

the amount of data collected in various IT systems has grown exponentially in the recent years. So the challenge rises how we can process those huge datasets with the fulfillment of strict time criteria and of effective resource consumption, usually posed by the service consumers. this problem is not yet resolved with the appearance of edge computing as widearea networking and all its well-known issues come into play and affect the performance of the applications scheduled in a hybrid edge-cloud infrastructure. In this paper, we present the steps we made towards network-aware big data task scheduling over such distributed systems. We propose different resource orchestration algorithms for two potential challenges we identify related to network resources of a geographically distributed topology: decreasing end-to-end latency and effectively allocating network bandwidth. the heuristic algorithms we propose provide better big data application performance compared to the default methods. We implement our solutions in our simulation environment and show the improved quality of big data applications.

关键词： Big data resource orchestration network latency bandwidth geo-distributed network topology

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Building Dynamic Mapping with CUPS for Next Generation Automotive Edge computing 8

Building Dynamic Mapping with CUPS for Next Generation Autom...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： Sun, Zhaoxia Du, Ping Nakao, Akihiro Zhong, Lei Onishi, Ryokichi Univ Tokyo Tokyo Japan Toyota Motor Co Ltd Toyota Japan

ISBN: (纸本)9781728148328

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. there are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud;(ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. the capabilities of our system have been quantified. the experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

关键词： Dynamic mapping CUPS Edge computing 5G

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