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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

共 24650 条记录，以下是691-700 订阅

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Intelligent Scheduling Strategy for Heterogeneous Multi-terminal Access Tasks for Complex Cloud-Edge Collaborative computing 4

Intelligent Scheduling Strategy for Heterogeneous Multi-term...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Wang, Haotian College of Software Nankai University Tianjin China

ISBN: (纸本)9798350337181

With the rapid development of network and mobile devices, the era of ten thousands households interconnection is gradually coming. The variety of applications is endless, putting higher demands on computing resources as well as latency. As a result, the task scheduling model of cloud-edge collaboration has emerged. In the current environment, due to the limited resources of edge nodes and the expanding volume of users and user data, how to allocate tasks to the best nodes becomes the most critical issue. To address this problem, this paper proposes the use of ant colony algorithm to optimize the cloud-edge collaborative task scheduling. The pheromone cumulative positive feedback method is mainly used to reflect the tendency of tasks to select processing devices. First, the probability of a processing device being selected by a task is calculated by pheromones. Then, a random number is used to determine the allocation strategy for each round of the cycle. Finally, the pheromone concentration of the processing device is updated and the minimum resource consumption as well as the optimal task assignment strategy is derived. The implementation results show that the ant colony algorithm can improve the response time, reduce the resource consumption, and improve the efficiency of multi-objective task scheduling. © 2023 IEEE.

关键词： Ant colony optimization

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Scalable Big Spatial Data Processing with SQL Query Compilation and distributed Morsel-driven parallelism

Scalable Big Spatial Data Processing with SQL Query Compilat...

引用

2024 IEEE international conference on Big Data, BigData 2024

作者： Sahni, Rahul Zhang, Xiaozheng Chatterjee, Sudip Ray, Suprio University of New Brunswick Fredericton Canada

ISBN: (纸本)9798350362480

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based *** present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus. © 2024 IEEE.

关键词： Structured Query Language

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distributed Yang Model Dependency Installation Solution 15

Distributed Yang Model Dependency Installation Solution

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Bipin, B. Nokia Networks Cns Ece Solutions Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

Sysrepo is a YANG(Yet another next Generation)-based configuration and operational state data store for Unix/Linux based applications. Applications will be able to use sysrepo to store their configurations, provided by YANG model. Instead of using normal configuration files, sysrepo will ensure data consistency of the data stored in the datastore and enforce data constraints defined by YANG model. YANG is a data modeling language for the definition of data sent over network management protocols such as the NETCONF and RESTCONF. While installing the YANG models into sysrepo, each YANG model follows a set of preconditions. These preconditions can be like, before installing a YANG model a set of specific yangs has to be there in the system. These dependent YANG models can be the ones which users manually have to install or sometimes it could be the ones which are system defined. While installing these, if the dependent ones are not present in sysrepo, then installation process fails. Here in this paper, a new approach is proposed where in the dependent YANG models will be ordered properly and parallel installation will be done which will fasten up the sysrepo and netopeer processes to startup. © 2024 IEEE.

关键词： Protocols Computational modeling Logic gates Data models Next generation networking

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A Synchronous parallel Method with Parameters Communication Prediction for distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

引用

19th EAI international conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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DEBS 2023 - Proceedings of the 17th ACM international conference on distributed and Event-based Systems

DEBS 2023 - Proceedings of the 17th ACM International Confer...

引用

17th ACM international conference on distributed and Event-based Systems, DEBS 2023

ISBN: (纸本)9798400701221

The proceedings contain 26 papers. The topics discussed include: a selective and biased choice of techniques for building a distributed data store;accelerating the performance of distributed stream processing systems with in-network computing;secure distributed data and event processing at scale: where are we now?;adaptive distributed streaming similarity joins;I will survive: an event-driven conformance checking approach over process streams;on improving streaming system Autoscaler behavior using windowing and weighting methods;practical forecasting of cryptocoins timeseries using correlation patterns;an exploratory analysis of methods for real-time data deduplication in streaming processes;considerations for integrating virtual threads in a Java framework: a Quarkus example in a resource-constrained environment;and discovery of breakout patterns in financial tick data via parallel stream processing with in-order guarantees.

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FlowValve: Packet Scheduling Offloaded on NP-based SmartNICs 42

FlowValve: Packet Scheduling Offloaded on NP-based SmartNICs

引用

42nd IEEE international conference on distributed computing Systems (ICDCS)

作者： Xi, Shaoke Li, Fuliang Wang, Xingwei Northeastern Univ Shenyang Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Enforcing scheduling policies at end-hosts with software schedulers suffers from high CPU consumption, low throughput, and inaccuracy. Offloading scheduling functions to the network interface card (NIC) provides a promising direction to address these problems. However, existing efforts in scheduling offloading suffer from inflexible on-NIC packet schedulers, which cannot execute complex hierarchies of network policies. In this paper, we present FlowValve, the first parallel packet scheduler for Network Processor (NP)-based SmartNlCs that offloads critical functions of Linux traffic control, including packet classifying and scheduling. The key insight behind FlowValve is to abstract inherent queues attached to the NIC interface (wire side) as a single FIFO queue and perform specialized tail drop to mix the FIFO queue with expected flow proportions. FlowValve takes advantage of on-chip multi-core parallelism and hardware accelerations to produce high throughput. Meanwhile, it substantially reduces CPU and memory burdens on endhosts. We prototype FlowValve on a Netronome Agilio SmartNIC and demonstrate its effectiveness against non-offloaded kernel schedulers and DPDK QoS Scheduler. We find that FlowValve outperforms both in accurately enforcing network policies while driving line rate performance (i.e., 40Gbps), which contributes to saving at least two CPU cores.

关键词： Scheduling algorithms Multicore processing Linux Wires Tail Traffic control parallel processing

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Towards Assessing Generative AI Based Empathic Systems

Towards Assessing Generative AI Based Empathic Systems

引用

2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ayesh, Aladdin Arevalillo-Herraez, Miguel Zoghlami, Asma School of Natural and Computing Sciences University of Aberdeen United Kingdom Universitat de València Departament d'Informàtica Spain London United Kingdom

ISBN: (纸本)9781665410205

Recent advances in generative AI epitomized by Large Language Models (LLMs) demonstrated remarkable capabilities in generating human-like text and understanding contextual nuances. In parallel, advances in domestic applications of AI continue to break new grounds bringing to the forefront the need to address the impact of such systems especially on users' well being through socially aware interactive intelligent systems. This paper takes one step forward in this endeavour to examine the potential of LLMs based systems to generate empathy in its responses. In doing so, we identified through initial tests the key factors to designing empathy triggering responses. We then designed a set of experiments to test systematically the impact each of these factors would have on triggering empathy. We analysed the results against the degree of expressed empathy and its perceived quality. © 2024 IEEE.

关键词： Generative adversarial networks

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HP3C 2023 - 2023 7th international conference on High Performance Compilation, computing and Communications

HP3C 2023 - 2023 7th International Conference on High Perfor...

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7th international conference on High Performance Compilation, computing and Communications, HP3C 2023

ISBN: (纸本)9781450399883

The proceedings contain 50 papers. The topics discussed include: cohort-based federated learning credit evaluation method in the metaverse;prediction of the number of postgraduate entrance examination applicants based on LSTM and statistical analysis method;a probe set determination method based on spanning tree algorithm;semantic deep mining method for recommendation based on small-world graphs;a novel distributed K-Means clustering algorithm for big text data;an optimized hybrid gauss-seidel smoother in AMG Solver of Hypre on Sunway many-core architecture;matrix multiplication with diagonals: structured sparse matrices and beyond;PEPC: parallel and extensible PC implementation for causal structure learning;and parallel optimization of SLIC algorithm for new-generation Sunway processors.

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DistSim: A performance model of large-scale hybrid distributed DNN training 23

DistSim: A performance model of large-scale hybrid distribut...

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20th ACM international conference on computing Frontiers (CF)

作者： Lu, Guandong Chen, Runzhe Wang, Yakai Zhou, Yangjie Zhang, Rui Hu, Zheng Miao, Yanming Cai, Zhifang Li, Li Leng, Jingwen Guo, Minyi Shanghai Jiao Tong Univ Shanghai Qi Zhi Inst Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400701405

With the ever-increasing computational demand of DNN training workloads, distributed training has been widely adopted. A combination of data, model and pipeline parallelism strategy, called hybrid parallelism distributed training, is imported to tackle the problem of deploying large-scale models. However, how to evaluate the hybrid strategy and the utilization of each device remains a challenge since existing works either profile on a real large-scale cluster with high time and money costs or only analyze a specific type of parallelism without considering the hybrid parallelism. In this work, we proposed DistSim, an event-based performance model to accurately analyze each device's computation and communication activities with low profiling costs. DistDim breaks down the model into events according to the given distributed strategy, which can be profiled on two nodes. Then DistSim leverages the hierarchy of different parallel strategies to generate the computation and communication event-flow from layer level to model level and finally the activity timeline of each device participating in training. Experiment shows that DistSim can reach <4% errors when predicting distributing training batch time and <5% errors when predicting a single device's activity time in various hybrid strategy settings. We also provide a use-case of DistSim, automatically evaluate and search the best distributed training strategy, and find a hybrid strategy with at most 7.37x throughput improvement.

关键词： distributed DNN training performance model

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Towards a Privacy-Aware Electric Vehicle Architecture 30

Towards a Privacy-Aware Electric Vehicle Architecture

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30th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Plappert, Christian Stancke, Jonathan Jaeger, Lukas Fraunhofer Inst Secure Informat Technol Darmstadt Germany

ISBN: (纸本)9781665469586

Connected vehicles need to generate, store, process, and exchange a multitude of information with their environment. Much of this information is privacy-critical and thus regulated by privacy laws like the GDPR ha Europe. In this paper, we analyze and rate exemplary data (flows) of the electric driving domain with regard to their criticality based on a reference architecture. We classify the corresponding ECUs based on their processed privacy-critical data and propose technical mitigation measures and technologies in form of generic privacy-enhancing building blocks according to the classification and requirements derived from the GDPR.

关键词： automotive cybersecurity and privacy threat mitigation electric vehicles connected car General Data Protection Regulation (GDPR) trusted computing Trusted Platform Module (TPM) Device Identifier Composition Engine (DICE) Hardware Security Module (HSM) ISO 15118

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