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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

共 140 条记录，以下是71-80 订阅

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distributed Trust Evaluation Mechanism of LEO Satellites for 6G Network 23

Distributed Trust Evaluation Mechanism of LEO Satellites for...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Hui Shi, DongCong Zhou, Ruijin Liao, Dan Zhang, Ming Zhou, Yuliang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Research Institute University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665494571

In 6G network, the LEO satellite communication is popular because of the lower development costs and lower transmission delay. However, due to the instability, openness and exposure of links between satellites, LEO satellite is vulnerable to internal attacks during data routing. Considering that trust management has a better performance in solving the internal attacks between satellite communications, this paper proposed a new distributed trust evaluation mechanism (DTEM) of LEO satellites based on D-S evidence theory. In the proposed trust evaluation mechanism, it evaluates the trust degree of satellite nodes by direct trust, indirect trust and aggregate trust. Then it detects the malicious satellite nodes for 6G networks and takes corresponding isolation measures. The simulations verifies that the proposed trust evaluation mechanism has superior performance in packet delivery rate and packet loss rate for data routing. © 2021 IEEE.

关键词： 6G mobile communication Satellites Smart cities Evidence theory Low earth orbit satellites Packet loss Routing

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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作者： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 IEEE.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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BOBBLE: A Mixed Routing-Granularity distributed Load Balancing for Data Center Networks 23

BOBBLE: A Mixed Routing-Granularity Distributed Load Balanci...

引用

作者： Xu, Zhengzhi Lu, Yifei Ma, Xu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665494571

In current Datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there's no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology. © 2021 IEEE.

关键词： Data centers Network topology Smart cities Load management Routing Mice Computer networks

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2021 IEEE 23rd international conference on High Performance computing and Communications, 7th international conference on Data Science and Systems, 19th international conference on Smart City and 7th international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

2021 IEEE 23rd International Conference on High Performance ...

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ISBN: (纸本)9781665494571

The proceedings contain 343 papers. The topics discussed include: three-level compact caching for search engines based on solid state drives;a fine-grained volley gesture recognition method with direction independence;universal adversarial attack against 3d object tracking;efficient hardware redo logging for secure persistent memory;a cost-efficient metadata scheme for high-performance deduplication systems;high speed true random number generator controlled by logistic map;characterization and implication of edge WebAssembly runtimes;frend for edge servers: reduce server number! keeping service quality!;on-the-fly servers placement for online multiplayer games in the fog;advanced architecture design of high-radix router based on chiplet integration and IP reusability;embrace the conflicts: exploring the integration of single port memory in systolic array-based accelerators;visual sensitivity aware rate adaptation for video streaming via deep reinforcement learning;semi-supervised federated learning with non-IID data: algorithm and system design;on-demand intelligent routing algorithms for the deterministic networks;and distributed service placement in ultra-dense edge computing: a game-theoretical approach.

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A Novel Path Planning-Aware Vehicular Task Offloading Strategy 23

A Novel Path Planning-Aware Vehicular Task Offloading Strate...

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作者： Liu, Yufei Zhao, Liang Yang, Kaiqi Li, Yunpeng School of Computing Shenyang Aerospace University Shenyang China Ciac China P&t Appliances Beijing China

ISBN: (纸本)9781665494571

In intelligent transportation system (ITS), the combination of vehicle network (VN) and mobile edge computing (MEC) can provide low delay networking services to vehicles. Since the quality of vehicle access network is mainly determined by location, the service quality of vehicle computation task unloading (CTO) should be considered in the process of vehicle path planning, so as to further improve the performance of a single vehicle in unloading throughput, delay, and cost. In this paper, with employing the SDN-based system architecture, we propose a novel path planning-aware vehicular task offloading strategy for connected vehicles in real-time by considering CTO to provide drivers a good driving experience. To achieve the efficient path planning, an effective road performance evaluation method is proposed, which combines the road capacity and the throughput of computation tasks into the driving index to evaluate the priority selected by vehicle drivers. The simulation results show that the proposed method can guarantee the driving time of the vehicle, while it can also give the driver a better quality of experience. © 2021 IEEE.

关键词： Performance evaluation Roads Simulation Systems architecture Throughput Real-time systems Path planning

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Message from the conference Chair

Proceedings - 2022 23rd ACIS International Summer Virtual Co...

引用

Proceedings - 2022 23rd ACIS international Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD-Summer 2022 2022年 XI页

作者： Hirata, Hiroaki Kyoto Institute of Technology Japan

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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Multi-level Virtualization and Its Impact on System Performance in Cloud computing 23rd

引用

23rd international conference on Computer Networks (CN)

作者： Lubomski, Pawel Kalinowski, Andrzej Krawczyk, Henryk Gdansk Univ Technol IT Serv Ctr Gdansk Poland Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (纸本)9783319392073;9783319392066

The results of benchmarking tests of multi-level virtualized environments are presented. There is analysed the performance impact of hardware virtualization, container-type isolation and programming level abstraction. The comparison is made on the basis of a proposed score metric that allows you to compare different aspects of performance. There is general performance (CPU and memory), networking, disk operations and application-like load taken into account. The tested technologies are, inter alia, VMware ESXi, Docker and Oracle JVM.

关键词： Virtualization Performance Benchmark

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Ab Initio Gene Identification Using Apache Spark 23

Ab Initio Gene Identification Using Apache Spark

引用

23rd IEEE international conference on Automation, Quality and Testing, Robotics (AQTR)

作者： Tatar, Simion-Daniel Sebestyen, Gheorghe Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca Romania

ISBN: (纸本)9781665479332

Computer-assisted gene identification in deoxyribonucleic acid (DNA) sequences is a challenging task, which requires intelligent pattern recognition algorithms as well as significant computing power. Gene recognition has increased importance today, when medical treatment of a patient should be adapted to the inherited features of each individual;these features may be identified through the patients' DNA. Gene recognition is also important for identify pathological organisms and their resistance to different treatments (e.g. antibiotics). In order to allow adaptive treatment of patients, gene identification must be fast, accessible and precise. In this paper we concentrate on formulating the problem of gene identification, on determining the most popular approaches and also on finding a parallelization procedure that can reduce the execution time of an identification algorithm.

关键词： gene identification Apache Spark distributed framework ab initio

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A Real-time Demand Response Strategy of Home Energy Management by Using distributed Deep Reinforcement Learning 23

A Real-time Demand Response Strategy of Home Energy Manageme...

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作者： Liu, Weirong Wang, Yujie Jiang, Fu Cheng, Yijun Rong, Jieqi Wang, Chenglong Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

Home energy management system (HEMS) autonomously schedules the energy usage of home electricity consuming devices to reduce the electricity cost based on real-time electricity price. However, it is very challenging to design an optimal energy management strategy under the uncertainty of the resident's behavior, outdoor temperature and renewable generation. To solve this problem, a real-time demand response (DR) strategy based on deep reinforcement learning (DRL) is proposed. Firstly, the real-time DR management problem is modeled as a Markov game, and then a home energy management algorithm is proposed based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). It is worth mentioning that the proposed algorithm builds the thermal dynamics model and takes energy storage system (ESS) into account, which improves the utilization of renewable power generation effectively. The simulation result based on real-world data demonstrates the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Renewable energy sources Schedules Costs Uncertainty Heuristic algorithms Simulation Reinforcement learning

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