Key Issues for Cloud Manufacturing Platform

2012 ◽  
Vol 472-475 ◽  
pp. 2621-2625 ◽  
Author(s):  
Zhen Nan Zhang ◽  
Pei Si Zhong
Keyword(s):  
Service Discovery ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Added Value ◽  
Future Research ◽  
Manufacturing Capability ◽  
Key Issues ◽  
Manufacturing Resource ◽  
Manufacturing Platform ◽  
Cloud Service Composition

In order to realize sharing and collaboration of manufacturing resource and manufacturing capability based on knowledge and to realize added value of manufacturing resource and manufacturing capability. In this paper, we propose an ontology-based architecture for cloud manufacturing platform. Meanwhile, several key issues for cloud manufacturing platform including semantic description of manufacturing resource and manufacturing capability, manufacturing cloud service advertisement, manufacturing cloud service discovery and manufacturing cloud service composition are studied in particular. Thus, the research provides foundation for the future research, development, implementation and application of cloud manufacturing platform.

A Framework for Correlation Relationship Mining of Cloud Service in Cloud Manufacturing System

2011 ◽  
Vol 314-316 ◽  
pp. 2259-2262 ◽  
Author(s):  
Hua Guo ◽  
Lin Zhang ◽  
Fei Tao
Keyword(s):  
Manufacturing System ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Cloud Services ◽  
Added Value ◽  
Correlation Relationship ◽  
Function Modules ◽  
Key Issues ◽  
Manufacturing Resource ◽  
Cloud Service Composition

As a new manufacturing paradigm, cloud manufacturing (CMfg) is proposed to realize the added-value and on-demand use of manufacturing resource and ability in the form of manufacturing services. Considering that there always exist correlations among cloud services (CS), which affect the cloud service composition (CSC). Hence, how to mine the correlations among CSs and apply them to CSC is a key issue for realizing the added-value. This paper presents a framework for correlation relationship mining for CSC. Four function modules for mining correlations among CSs are analyzed, and the involving key issues were preliminarily discussed as well.

Research on the Cloud Manufacturing Service Discovery for Industry Manufacturing System Based on Ontology

2013 ◽  
Vol 712-715 ◽  
pp. 2639-2643 ◽  
Author(s):  
Chen Yang ◽  
Zhong Jie Wang
Keyword(s):  
Service Discovery ◽  
Manufacturing System ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Important Indicator ◽  
Service Oriented ◽  
Manufacturing Resource ◽  
Resource Data ◽  
Manufacturing Platform ◽  
Automatic Matching

With the development of manufacturing globalization, cloud manufacturing is becoming a hotspot of network manufacturing. In cloud manufacturing, how to response to users services demands fast and accurately is an important indicator to evaluate the performance of this system. To achieve a cloud manufacturing platform for industry manufacturing system, this paper proposes service-oriented platform architecture based on semantic. Also, to realize the manufacturing resource data virtualization, the semantic description of manufacturing cloud and the combined manufacturing ontology is established. To fulfill the requirements of providing the fit cloud service or service combination, a cloud manufacturing service discovery model is investigated on focus, the organic decomposition and combination of manufacturing cloud is completed by ontology reasoning, and intelligent search and automatic matching of manufacturing cloud is realized.

scholarly journals Evaluating the QoS Cognizance in Composition of Cloud Services: A Systematic Literature Review

2018 ◽  
Vol 7 (4.6) ◽  
pp. 141
Author(s):  
A V L N Sujith ◽  
Dr. A Rama Mohan Reddy ◽  
Dr. K Madhavi
Keyword(s):  
Service Composition ◽  
Service Discovery ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Future Research ◽  
Promising Area ◽  
Cloud Service Composition ◽  
Rapid Deployment

Enterprise level computing constantly investigates novel approaches that maximize their profits and minimize their expenses. With the rapid growth of cloud computing XaaS – ‘anything as a service’, service providers are enabled with the rapid deployment of virtual services to service requestors. Because of the enormous growth in the variety of the services and based on the demand of the virtualized resources, cloud service providers are facing tough competition to facilitate the composite service requests made by the service requestors. QoS (Quality of Service)  is considered to be a preliminary factor while composing a new cloud service out of heterogeneous and distributed atomic services. Therefore service composition is promising area that focuses on the design and development of the automated approaches to deal with diverse phases of service composition techniques that include service discovery, negotiation, service selection and optimization of the atomic services. This paper provides anatomy of  existing studies addressing the problem of cloud service composition that enable to identify intended objectives of the technique along with diverse QoS aware problem solving approaches. Furthermore, the key areas of the improvement in cloud service composition are identified for future research. 

scholarly journals A Bilayer Resource Model for Cloud Manufacturing Services

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Linan Zhu ◽  
Yanwei Zhao ◽  
Wanliang Wang
Keyword(s):  
Manufacturing Industry ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Service Description ◽  
Resource Model ◽  
Attribute Model ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Manufacturing Platform ◽  
Resource Service

Cloud Manufacturing and Cloud Service is currently one of the main directions of development in the manufacturing industry. Under the Cloud Manufacturing environment, the characteristics of publishing, updating, searching, and accessing manufacturing resources are massive, complex, heterogeneous, and so forth. A bilayer manufacturing resource model with separation of Cloud End and Cloud Manufacturing Platform is proposed in this paper. In Cloud End, manufacturing resources are divided into single resource and complex resource, and a basic data model of manufacturing resources oriented to enterprise interior is established to store the physical characteristics. In Cloud Manufacturing Platform, a resource service attribute model oriented to actual users is established to store the service characteristics. This model is described in detail and realized with stateful Web Service Description Language (WSDL) document. An example is provided for illustrating the implementation of the concept.

A Model of Competitive Hybrid Granularity Manufacturing Resource and its Unify Cloud Service Modeling

2014 ◽  
Vol 670-671 ◽  
pp. 1556-1561 ◽  
Author(s):  
Xuan Liu ◽  
Wei Tan
Keyword(s):  
Business Process ◽  
Service Discovery ◽  
Manufacturing Industry ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Basic Function ◽  
Cloud Services ◽  
Utilization Rate ◽  
Service Model ◽  
Manufacturing Resource

With the application of cloud computing technology in the manufacturing industry, there appear all sorts of the manufacturing cloud services on the network. Basic of manufacturing cloud service is manufacturing resource, and the business process is the business context of the manufacturing resource. In order to improve the utilization rate of cloud services, a model for the hybrid granularity manufacturing resources with strong competitiveness is put forward, including basic information, basic function, business process and quality of service of manufacturing resource, and based on which cloud manufacturing service model is construct. The service discovery experiment is designed based on the manufacturing cloud service model, and the experimental results prove that the cloud service from strong competitive hybrid granularity manufacturing resource has more competitive power and higher utilization.

Automatic Manufacturing Cloud Service Composition Based on AI Plan

2014 ◽  
Vol 513-517 ◽  
pp. 990-993 ◽  
Author(s):  
Pei Si Zhong ◽  
Shao Qi Zhu ◽  
De Jie Huang ◽  
Hai Liang Xin
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Search Method ◽  
Added Value ◽  
Service Environment ◽  
Manufacturing Resources ◽  
Cloud Service Composition ◽  
Automatic Manufacturing

Manufacturing cloud service composition is the key way to improve the utilization of manufacturing resources and manufacturing capabilities, realize added value and efficiency of manufacturing resources and manufacturing capabilities. It has great significance on cloud manufacturing implementation and carry. Therefore, the paper presents automatic forwarding search method called AMCSC-HFS for manufacturing cloud service composition based on AI plan. The main purpose is to support meeting the actual need of large-scale and dynamic cloud manufacturing cloud service environment.

scholarly journals IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Li-Nan Zhu ◽  
Peng-Hang Li ◽  
Xiao-Long Zhou
Keyword(s):  
New Product Development ◽  
Service Composition ◽  
Manufacturing System ◽  
Cloud Service ◽  
Optimization Method ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Parallel Hybrid ◽  
Manufacturing Service Composition ◽  
Cloud Service Composition

Cloud manufacturing (CMfg) is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on-demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

Research on the Key Technology of Manufacturing Services Modeling under Cloud Manufacturing Environments

2014 ◽  
Vol 624 ◽  
pp. 687-693
Author(s):  
Pei Zhi Liu ◽  
Jun Ji ◽  
Wei Yan Chai ◽  
Xiao Chuan Zhao
Keyword(s):  
Cloud Manufacturing ◽  
Service Model ◽  
Function Model ◽  
Key Technology ◽  
Manufacturing Capability ◽  
Manufacturing Resources ◽  
Manufacturing Platform ◽  
And Function ◽  
Manufacturing Environments

To realize sharing and synergy between manufacturing resources and manufacturing capability in cloud manufacturing, the manufacturing service model is divided into function model and non-function model. Elements and description of function model is given, and function model determines manufacturing services’ combination planning. Also the composition of non-function model and each part’s weight is presented, and non-function model is the basis to evaluate the priority of manufacturing services. For the case of resource in cloud manufacturing services, sharing ontology and private ontology are researched. Sharing ontology is common description of the whole manufacturing domain, and private ontology is individual description of a specific manufacturing platform. The transformation between sharing ontology and private ontology provides a way for isomerism resources to invoke each other.

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