scholarly journals The Essence of Cloud Manufacturing

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Tolulope J. Ashaolu ◽  
Abayomi Ajayi-Majebi ◽  
Sarhan M. Musa
Keyword(s):  
High Performance ◽  
Manufacturing System ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Computing Technology ◽  
Knowledge Based ◽  
Service Oriented ◽  
Manufacturing Resources ◽  
Manufacturing Software ◽  
Performance Computing

Cloud manufacturing is a service-oriented, knowledge-based smart manufacturing system. It is integrated with cloud computing technology, Internet of Things, and high-performance computing. Through cloud manufacturing, manufacturing resources (such as manufacturing software tools, manufacturing equipment, and manufacturing capabilities) are “virtualized” and offered as consumable in the same way as electricity, gas, and water. The manufacturing resources are provided to users as services over the Internet (cloud) in a pay-as-you-go manner. This paper presents a brief introduction to cloud manufacturing.

The State of the Art of Cloud Manufacturing and Future Trends

2013 ◽  
Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm
Keyword(s):  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Cloud Manufacturing ◽  
Advanced Manufacturing ◽  
Future Trends ◽  
Research Directions ◽  
Knowledge Based ◽  
Information And Communication ◽  
Service Oriented ◽  
Manufacturing Software

Cloud manufacturing has emerged as a new manufacturing paradigm, which combines technologies (such as Internet of Things, Cloud computing, semantic Web, virtualisation and service-oriented technologies) with advanced manufacturing models, information and communication technologies. It aims to be networked, intelligent, service-oriented, knowledge-based and energy efficient, and promises a variety of benefits and advantages by providing fast, reliable and secure on-demand services for users. It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities, will then be made accessible to presumptive consumers on a worldwide basis. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with future trends and research directions in Cloud manufacturing.

Research on the Knowledge-Based Multi-Dimensional Information Model of Manufacturing Capability in CMfg

2012 ◽  
Vol 472-475 ◽  
pp. 2592-2595 ◽  
Author(s):  
Yong Liang Luo ◽  
Lin Zhang ◽  
Kui Ping Zhang ◽  
Fei Tao
Keyword(s):  
Collaborative Work ◽  
Information Model ◽  
Cloud Manufacturing ◽  
Three Dimensions ◽  
Formal Description ◽  
On Demand ◽  
Knowledge Based ◽  
Manufacturing Capability ◽  
Service Oriented ◽  
Manufacturing Resources

Cloud manufacturing (CMfg) is a new service-oriented, highly efficient, lowly consumption knowledge based, and intelligent networked manufacturing model. It aims to achieve on-demand use, dynamic collaborative work, and circulation of manufacturing resources and manufacturing capability(MC). In order to achieve the above goal proposed, how to realize the formal description of MC is the key point in CMfg. This paper emphasizes the multi-dimensional information model of MC based on knowledge. According to the requirement and characteristics of manufacturing, the concept and connotation of MC were presented firstly in this paper, then the multi-dimension information model for MC was proposed based on knowledge, it contains three dimensions, such as elements dimension, relationship dimension, evaluation dimension.

Cloud Manufacturing Resource Management Based on Metadata

2015 ◽  
Author(s):  
Chun Zhao ◽  
Lin Zhang ◽  
Xuesong Zhang ◽  
Liang Zhang
Keyword(s):  
Resource Management ◽  
Cloud Manufacturing ◽  
State Monitoring ◽  
Knowledge Resources ◽  
Centralized Management ◽  
Service Oriented ◽  
Manufacturing Resources ◽  
Resource Data ◽  
Manufacturing Platform ◽  
Management Approaches

Centralized management and sharing of manufacturing resources is one of the important functions of cloud manufacturing platform. There are many kinds of manufacturing resources, centralized management, optimized scheduling, quick searching for various manufacturing resources become important issues in a cloud manufacturing platform. This paper presents a resource management model based on metadata to realize the access and unified management of the hardware resources, software resources and knowledge resources. Two management approaches respectively for static and dynamic resource data are introduced to realize resource state monitoring and real-time information collecting. On this basis, the relationship between static and dynamic data is determined and service-oriented of resources is realized.

High Performance Computing on Mobile Devices

2016 ◽  
pp. 334-348
Author(s):  
Atta ur Rehman Khan ◽  
Abdul Nasir Khan
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Mobile Devices ◽  
High Performance ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Computing Technology ◽  
Wide Range ◽  
Performance Computing

Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.

A Hybrid Resource Reservation Method for Workflows in Clouds

2012 ◽  
Vol 4 (4) ◽  
pp. 1-21
Author(s):  
Tyng-Yeu Liang ◽  
Fu-Chun Lu ◽  
Jun-Yao Chiu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Performance ◽  
Resource Reservation ◽  
Scientific Workflows ◽  
Service Oriented ◽  
Time And Energy ◽  
Gpu Architecture ◽  
Performance Computing ◽  
Oriented System

QoS and energy consumption are two important issues for Cloud computing. In this paper, the authors propose a hybrid resource reservation method to address these two issues for scientific workflows in the high-performance computing Clouds built on hybrid CPU/GPU architecture. As named, this method reserves proper CPU or GPU for executing different jobs in the same workflow based on the profile of execution time and energy consumption of each resource-to-program pair. They have implemented the proposed resource reservation method on a real service-oriented system. The experimental results show that the proposed resource reservation method can effectively maintain the QoS of workflows while simultaneously minimizing the energy consumption of executing the workflows.

scholarly journals GITIRBio: A Semantic and Distributed Service Oriented- Architecture for Bioinformatics Pipeline

2015 ◽  
Vol 12 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Luis F. Castillo ◽  
Germán López-Gartner ◽  
Gustavo A. Isaza ◽  
Mariana Sánchez ◽  
Jeferson Arango ◽  
...  
Keyword(s):  
High Performance ◽  
Service Oriented Architecture ◽  
Command Line ◽  
Bioinformatics Pipeline ◽  
Front End ◽  
First Approximation ◽  
Service Oriented ◽  
Computational Resources ◽  
Multiple Sequences ◽  
Performance Computing

Summary The need to process large quantities of data generated from genomic sequencing has resulted in a difficult task for life scientists who are not familiar with the use of command-line operations or developments in high performance computing and parallelization. This knowledge gap, along with unfamiliarity with necessary processes, can hinder the execution of data processing tasks. Furthermore, many of the commonly used bioinformatics tools for the scientific community are presented as isolated, unrelated entities that do not provide an integrated, guided, and assisted interaction with the scheduling facilities of computational resources or distribution, processing and mapping with runtime analysis. This paper presents the first approximation of a Web Services platform-based architecture (GITIRBio) that acts as a distributed front-end system for autonomous and assisted processing of parallel bioinformatics pipelines that has been validated using multiple sequences. Additionally, this platform allows integration with semantic repositories of genes for search annotations. GITIRBio is available at: http://c-head.ucaldas.edu.co:8080/gitirbio

Product Life Cycle-Oriented Cloud Process Management Research

2011 ◽  
Vol 120 ◽  
pp. 456-459 ◽  
Author(s):  
Li Sun ◽  
Jian Min Fan ◽  
Zi Qin Ma ◽  
Xiu Lun Wang ◽  
Hao Tan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Life Cycle ◽  
Product Life Cycle ◽  
Process Management ◽  
Cloud Manufacturing ◽  
Product Life ◽  
Knowledge Based ◽  
Network Process ◽  
Service Oriented ◽  
Cloud Process

Based on the philosophy of cloud computing and cloud manufacturing, cloud process management, a new intelligently network process management model, is proposed. And it is service-oriented, highly efficient, and knowledge-based. On the base of the study of contemporary process management, cloud computing, and cloud manufacturing, product life cycle-oriented cloud process management is researched with the survey of a large state-owned enterprise. The cloud process management is initially defined. The structure model of cloud process management is expounded. And the application mode of product life cycle-oriented cloud process management is initially put forward. These establish the foundation for further research.

Resource Virtualization Model in Cloud Manufacturing

2010 ◽  
Vol 143-144 ◽  
pp. 1250-1253 ◽  
Author(s):  
Lei Wu
Keyword(s):  
Resource Sharing ◽  
Cloud Manufacturing ◽  
Application Layer ◽  
Resource Virtualization ◽  
Service Oriented ◽  
Model Cloud ◽  
Manufacturing Resources ◽  
Support Resource ◽  
Future Work ◽  
Manufacturing Problems

To solve more complex manufacturing problems and perform larger scale collaborative manufacturing, a new service-oriented networked manufacturing model—Cloud Manufacturing is presented. The paper presents a resource virtualization model to support resource sharing in cloud manufacturing environment. It can be decomposed into four layers: manufacturing resources layer, concrete web service layer, logical service layer and application layer. The relationships of every layer are discussed in detail. At last, we make a conclusion and put forward the future work.

A Modular Finite Element Parallel Fluid Applications Simulator

1999 ◽  
Author(s):  
K. L. Wong ◽  
A. J. Baker
Keyword(s):  
Finite Element ◽  
High Performance ◽  
Computational Mechanics ◽  
Navier Stokes ◽  
Computing Technology ◽  
Collaborative Project ◽  
University Of Tennessee ◽  
The University ◽  
Benchmark Solutions ◽  
Performance Computing

Abstract With the rapid advances in high-performance computing technology in recent years, researchers at the University of Tennessee have initiated a collaborative project to address the theoretical, algorithmic, and numerical implementation issues of computational mechanics simulations. To meet the challenge, a Parallel Interoperable Computational Mechanics Simulator System (PICMSS) has been established. Such effort entails the development of a modular finite element platform capable of admitting diverse CFD formulations, as well as closure models for physics phenomena, as required/derived by the users operating on supercomputers and networks of PC clusters. Incompressible Navier Stokes Benchmark solutions via a velocity-vorticity formulation are proposed to examine the integrity and scalability of PICMSS.

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