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检索条件"任意字段=2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering"

共 1401 条记录，以下是401-410 订阅

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Construction of ODR platform of engineering construction laws and regulations based on cloud computing technology

Construction of ODR platform of engineering construction law...

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Big Data & Artificial Intelligence & Software engineering (ICBASE), international conference on

作者： Yunzhe Gao Xiujuan Sun Xi'an Eurasia University Xi 'an Xincheng District Comprehensive and Grid Service Management Center

ISBN: (纸本)9781665427104

With the implementation of the strategy of "governing the country by law", it has promoted the normal operation of all walks of life in the whole society under the supervision of legal system. Especially in the construction industry, the traditional construction disputes have a long settlement cycle and a single solution, which directly affects the construction quality of the project and the basic interests of the masses. The ODR platform of engineering construction laws and regulations based on cloud computing technology provides a new solution channel for various disputes in the construction industry, and it is also an important means to assist the reasonable application of engineering construction laws and regulations.

关键词： Computers cloud computing Costs Law Regulation Personnel Construction industry

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Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture 2

Comparative evaluation of performance and scalability of con...

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2nd international conference on Computational Intelligence and Energy Advancements, ICCIEA 2021

作者： Pandey, Sunil Nagwani, Naresh Kumar Verma, Shrish Dept of Computer Science and Engineering NIT Raipur India Dept of Electronics and Communications Engineering NIT Raipur India

The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neur

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Efficient Graph Algorithms for Mapping Tasks to Processors 2nd

Efficient Graph Algorithms for Mapping Tasks to Processors

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2nd EAI international conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2019

作者： Kalyur, Sesha Nagaraja, G.S. Department of Computer Science and Engineering R.V. College of Engineering VTU Bangalore India

ISBN: (纸本)9783030475598

parallelization is the process of finding instruction clusters called tasks, which can be executed in concurrent fashion, on multiple processing elements. The objective of parallel execution is to reduce program run time, by overlapping the processing of these tasks. However these tasks are not entirely independent, and generally depend on data generated by peers. One of the objectives of effective parallelization is to limit this inter-task communication, an overhead which can limit the speedup realized through parallelization. Since this inter-task communication cannot be entirely avoided, judiciously mapping the tasks among the processors of a parallel machine, assumes utmost importance. Performance of a parallel program, is also determined by how uniformly the processors of the target machine are loaded with tasks, in situations where the tasks outnumber the processors. This activity is normally referred to as the load balancing. The problem of generating parallel tasks out of a sequential program is well studied, but the related problem of effectively mapping the concurrent tasks, to the processors of a parallel machine, still needs to be researched. This research work involved studying the problem of mapping specific tasks to individual processors, referred to here as the Processor Task Mapping and finding effective solutions. Previous solutions to the mapping problem were non-deterministic, sub-optimal, and incomplete, since their focus was mainly on the task schedules and load balancing criteria and mostly ignoring the communication profile of the tasks to drive the mapping decision. This was the main driving force for taking up our research. We present here the outcomes of this study namely, several efficient graph based algorithms to perform this mapping effectively. These algorithms are general and are applicable to machine topologies of diverse architectures, including the Shared Memory Multiprocessor, distributed Multiprocessor and the Non-Uniform Memor

关键词： Topology

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A Study for UAV Autonomous Safe Landing-Site Selection on Rough Terrain 2021

A Study for UAV Autonomous Safe Landing-Site Selection on Ro...

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2nd international conference on computing and Data Science, CONF-CDS 2021

作者： Zheng, Wenlong Yi, Jianjun Xiang, Haojie Zhou, Bo Wang, Danwei Zhao, Changchun Department of Mechanical Engineering East China University of Science and Technology Shanghai200237 China Intelligent Unmanned System Control Technology Joint Lab Shanghai201109 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (纸本)9781450389570

Autonomous safe landing of UAV is an important function in many scenarios such as force landing and delivery. This paper proposes a method to autonomously select a safe landing site for vertical take-off and landing (VTOL) UAV based on point cloud, which can minimize combined risks posed during touch down at the chosen landing site. The most suitable landing site of a landing zone is selected according to the terrain complexity. In this paper, (1) fine-grained grid elevation map converted from the terrain point cloud is used to calculate the potential risk such as slope, roughness and maximum height difference. (2) A comprehensive risk model is designed to consider all above risks to recognize obstacles and risk areas, and combine the flight distance factors to obtain the final cost map. (3) We process cost map as image by OpenCV to accelerate the processing and reduce reaction time. Terrain point clouds of simulation scene and real world are used for experiments and experimental results show that the selected landing sites can meet the safety requirements, which demonstrate the effectiveness and feasibility of our proposed method. © 2021 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Resource Allocation with Score for Reliable Task Scheduling in cloud computing Systems 2

Efficient Resource Allocation with Score for Reliable Task S...

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2nd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020

作者： Lepakshi, Vijayalakshmi A. Prashanth, C.S.R. Maharani Lakshmi Ammanni College for Women Department of Computer Science Bangalore India New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781728141671

parallel task execution in a heterogeneous cloud computing system emerges as NP-complete problem and in the literature, many heuristics behave differently when deployed in various environments. Efficient resource allocation improves reliability and leads to the completion of jobs and minimization of delays. In general, static task scheduling algorithms consider the earliest finish time (EFT) of the task to minimize the makespan. In this work, we propose a new heuristic called Efficient Resource Allocation with Score (ERAS) for reliable task scheduling in cloud computing systems, which considers temporal operational availability of Virtual Machines (VM) by considering various types of delays and EFT to assign a normalized score to the processor for scheduling tasks. Here, the allocation of VMs to tasks is based on a score given to each VM, by considering multiple criteria. The results show that ERAS algorithm gives better performance with increased reliability when compared to existing algorithms that consider only EFT for allocation. © 2020 IEEE.

关键词： cloud computing

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distributed storage method of College Students' Digital Archives Based on cloud computing

Distributed storage method of College Students' Digital Arch...

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Information Science and Education (ICISE-IE), international conference on

作者： Yiwu Xu Wenjing Fu Shandong Agriculture And Engineering University Jinan China Wuhan University Of Science And Technology Wuhan China

ISBN: (纸本)9781665438308

The existing distributed storage method of college students’ digital files has the problem of imperfect storage medium structure, which leads to the execution time of the method being too long. A distributed storage method of college students’ digital files based on cloud computing is designed. College students with the information dependencies, chain of digital archive information resources characteristics, through the storage management equipment or software access control data, adjust the structure of a storage medium, quantify the cluster of storage medium load, using cloud computing resource integration model was constructed, after the catalog file directory information uploaded to the central archives, optimization of distributed storage mode. Experimental results: The average execution time of the distributed storage method of college student digital files in this paper and the other two distributed storage methods of college student digital files are respectively 1979 ms, 2561ms, 2624 ms, proving that the distributed storage method of college student digital files integrated with cloud computing has higher application value.

关键词： cloud computing Information science Information resources Design methodology Storage management Education distributed databases

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Path Planning of Mobile Robot Based on an Improved Genetic Algorithm

Path Planning of Mobile Robot Based on an Improved Genetic A...

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Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Zenghua Chen Gang Xiong Sheng Liu Zhen Shen Yue Li State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Automobile Chang'an University Xi'an China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.

关键词： Sociology Clustering algorithms Path planning Planning Trajectory Statistics Collision avoidance

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A microservices-based IoT applications in Edge computing environments

A microservices-based IoT applications in Edge computing env...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Sunil Kumar Agarwal S M Ramesh A Arun Kumar Sachin Yadav M Nagalakshmi Prabhishek Singh S. S. Jain Subodh P.G. College Rajasthan India KPR Institute of Engineering and Technology Coimbatore India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad India G L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Bennett University Greater Noida India

ISBN: (纸本)9781665474177

With the improvement of Internet of Things, the scope of local area devices is expanding, and the cloud data center burden builds; some deferral delicate contributions can’t be answered to opportune, which outcomes in a brought down good of transporter (QoS). Edge computing (EC) supports the utilization of its contributions with low idleness, place perception and versatility help to compensate for the risks of distributed computing, and has gotten a colossal energy as of late. Notwithstanding, the powerfully modifying fine of transporter (QoS) may also outcome in screw ups of QoS-mindful guidance and organization of EC administrations, which strikingly debases clients’ pride and nullifies the gifts of MEC. To handle this issue, with the guide of pondering client related and administration related context-oriented components and an assortment of EC contributions booking situations, we suggest two setting mindful QoS expectation plans for EC administrations. The main plan is intended for the circumstances when EC contributions are planned for continuous, which conveys two setting mindful constant QoS assessment strategies. One strategy can assess the ongoing multi-QoS of EC contributions and the different procedure can appraise the continuous outfitted QoS of EC administrations. The 2d plan is intended for the circumstances when EC contributions are planned for what is in store. This plan comprises of two setting mindful QoS expectation strategies. One method can anticipate the multi-QoS of EC contributions and the different procedure can foresee the furnished QoS of EC administrations. At long last, versatile QoS expectation strategies are created in the gentle of qualities of the proposed QoS forecast techniques. As indicated by these systems, the most stunning QoS forecast approach can be planned. Broad investigations are completed to approve our proposed strategies and to display their exhibition.

关键词： Data centers cloud computing Microservice architectures Quality of service Organizations Fasteners Internet of Things

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Terms Development of Additive Manufacturing

Terms Development of Additive Manufacturing

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Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Tariku Sinshaw Tamir Gang Xiong Jingchao Jiang Zhen Shen Ehtisham Lodhi Hub Ali Li Wan Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Ten Dimensions (Guangdong) Technology Co. Ltd Foshan China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development.

关键词： Fabrication Additives Three-dimensional displays Three-dimensional printing Market research Digital twins Biological materials

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Comparison Study of Load Balancing Algorithm 2

Comparison Study of Load Balancing Algorithm

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2nd IEEE international conference on Advances in computing, Communication Control and Networking, ICACCCN 2020

作者： Rai, Himanshu Ojha, Sanjeev Kumar Nazarov, Alexey Moscow Institute of Physics and Technology Dept. of Radio Engineering and Cybernetics Moscow Russia

ISBN: (纸本)9781728183374

cloud computing is in vogue and, playing a compelling role in internet world. The cloud computing is an enumerate version of grid and cluster computing. cloud computing are provides the utility of computer services such as PaaS, SaaS, and IaaS, and all services provided in a cost effective way. This cloud computing services performance is an important factor, if this utility service is slow and cost is high then it will obsolete in internet world. Hence researcher keep researched on performance of cloud computing services. The cloud server, when receive lots of service request then server need to assign load equally and this process is called load balancing. Load balancer are two type, hardware and software load balancer. The hardware load balancer implementation cost is so high therefore these types of load balancer are not popular. The software load balancer is cost effective;therefore, cloud servicer adopts these types of load balancer. In this paper our discussion is about load balancing and performance factor those are helping to manage the resources such as CPU, memory, secondary storage. It's an important of any cloud service to provide load balancing in an effective manner. In this paper we are going to do comparative study and analysis of existing major load balancing algorithms, and study and analyze for the algorithms various performance factor such as peak hour usage and distribution of services. Our focus on LB challenges in cloud computing, high concern on improvement of performance factor such resources utilization, response time, and throughput. In cloud computing network consist of different servers, storage devices and network unit. © 2020 IEEE.

关键词： cloud computing

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