Contents

Cover

Half Title page

Title page

Copyright page

Dedication

Acknowledgements

Preface

Abbreviations

Chapter 1: Introduction to Manufacturing Grid

1.1 Introduction

1.2 Proposal of Manufacturing Grid

1.3 Concept of MGrid

1.4 Basic Features of MGrid

1.5 The Connotation of MGrid

1.6 Comparison between MGrid and Networked Manufacturing

1.7 Comparison between MGrid and Computing Grid

1.8 Key Research Contents and Technologies of MGrid

1.9 Summary

Chapter 2: Resource Service Optimal-Allocation System in MGrid

2.1 Introduction

2.2 The Architecture of MGrid

2.3 MGrid Collaborative Manufacturing Platform

2.4 MGrid Resource Service Optimal-Allocation System (MGRSOAS)

2.5 The Key Issues and Technologies for Realizing RSOAS

2.6 Summary

Chapter 3: Digital Description of MGrid Resource Service

3.1 Introduction

3.2 Classification of MGrid Resource Service and Its Application

3.3 Requirements of DDoRS in MGrid

3.4 MGrid and Ontology

3.5 Establishing the Method of MGrid-Ontology

3.6 Selection of Describing Language for MGrid-Ontology

3.7 MGrid Ontology

3.8 DDoRS Based on MGrid-Ontology

3.9 Application Case: MGrid-Ontology Based MGrid Resource Service Discovery

3.10 Summary

Chapter 4: MGrid Resource Service Match and Search

4.1 Introduction

4.2 Related Works

4.3 Framework of Resource Service Match and Search in MGrid

4.4 SMAs: Similarity Matching Algorithms (SMAs)

4.5 RS-Matcher: Resource Service Matcher

4.6 Case Study

4.7 Performance Results and Discussion

4.8 Summary

Chapter 5: Resource Service QoS Modeling and Evaluation

5.1 Introduction

5.2 Related Works

5.3 Evaluation Indices System of MGrid Resource Service

5.4 Evaluation of SEIs and IEIs

5.5 Classification and Modeling of MGrid QoS

5.6 Evaluation of MGrid QoS Attribute Parameter

5.7 Application Case: QoS-based MGrid Resource Service Management

5.8 Summary

Chapter 6: Resource Service Trust-QoS Evaluation

6.1 Introduction

6.2 Related Works

6.3 Resource Management and Trust Relationship Management in MGrid

6.4 MGrid Resource Service Trust-QoS Relationship Model

6.5 MGrid Resource Service Trust-QoS Evaluation Model

6.6 Data Structure Design

6.7 Trust-QoS Evaluating and Updating Algorithms

6.8 Real-time and Dynamical Updating Algorithm of Trust-QoS Degree

6.9 Trust-QoS Evaluation Case Study

6.10 Application Case: Trust-QoS Based MGrid Resource Service Scheduling

6.11 Summary

Chapter 7: Resource Service Optimal-selection and Composition Framework

7.1 Introduction

7.2 MGrid-RSOSCF: MGrid Resource Service Optimal-selection and Composition

7.3 T-Layer: Task Layer

7.4 S-Layer: Resource Service Match and Search Layer

7.5 Q-Layer: Resource Service QoS Synthetically Processing Layer

7.6 O-Layer: Resource Service Optimal-selection Layer

7.7 C-Layer: Resource Service Composition Layer

7.8 Summary

Chapter 8: Resource Service Optimal-selection Based on Intuitionistic Fuzzy Set and Non-functionality QoS

8.1 Introduction

8.2 Framework of Resource Service Selection

8.3 Resource Service Optimal-selection Based on IFS in MGrid

8.4 Case Study

8.5 Performance Analysis and Discussion

8.6 Summary

Chapter 9: Correlation Relationship Management in Resource Services Composition

9.1 Introduction

9.2 Related Works

9.3 Motivation

9.4 Correlation Relationship among Resource Services

9.5 QoS Computation Model of Correlation-aware Resource Services Composition

9.6 Case Study: Correlation-aware Resource Services Composition

9.7 Summary

Chapter 10: Resource Service Composition Optimal-selection

10.1 Introduction

10.2 Problem Formulation and Review

10.3 Review of Standard PSO

10.4 Improved PSO for MO-MRSCOS Problem

10.5 Performance Analysis and Discussion

10.6 Summary

Chapter 11: Resource Services Composition Flexibility

11.1 Introduction

11.2 Related Works

11.3 The Analysis, Definition and Classification of RSC Flexibility

11.4 The Measurement of RSC Flexibility

11.5 Case Study and Experiment Results

11.6 Summary

Chapter 12: Resource Services Composition Network

12.1 Introduction

12.2 Scale-free Network (SFN)

12.3 The Theoretical Hypothesis: Composition Service Network is a Scale-free Network

12.4 Concepts and Definition in CoRCS-Net

12.5 The Evolving Behavior of CoRCS-Net

12.6 Theoretical Proof of the Scale-free Characteristics of CoRCS-Net

12.7 Summary

Chapter 13: Failure Detection and Recovery in Resource Service Optimal-Allocation

13.1 Introduction

13.2 Related Works

13.3 Define and Classification of MGrid Failure

13.4 Architecture of MGrid Failure Management System

13.5 Detection of MGrid Failure

13.6 MGrid Failure Recovery Based on ECA Rules

13.7 Implementation and Simulation

13.8 Conclusion

Chapter 14: Summary of the Application of Grid Technology in Manufacturing

14.1 Introduction

14.2 Review of MGrid Theories

14.3 Investigation of Application Research on MGrid

14.4 Key Future Research Issues

14.5 Summary

Chapter 15: Cloud Manufacturing: Development and Commerce Realization of MGrid

15.1 Introduction

15.2 Concept and Architecture of Cloud Manufacturing

15.3 Core Enabling Technologies for Cloud Manufacturing

15.4 Typical Characteristics of Cloud Manufacturing

15.5 Difference and Relationship between Cloud Computing and Cloud Manufacturing

15.6 Classification of Cloud Manufacturing Service Platform

15.7 Key Technologies and Main Research Contents of Cloud Manufacturing

15.8 Key Advantages and Challenges of Cloud Manufacturing

15.9 Summary

Bibliography

Index

Also of Interest

Resource Service Management in Manufacturing Grid System

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Acknowledgements

This book is a summary of Dr. Tao’s research on resource service management in manufacturing grid (MGrid) and cloud manufacturing (CMfg) system during his study and work from September 2005 to August 2011 in Wuhan University of Technology (WHUT), the University of Michigan-Dearborn (UMD), and Beihang University (BUAA). Therefore, Dr. Tao would like to send special thanks to Professors YF Hu, ZD Zhou, MZ Yang, BY Shen, and YF Ding in WHUT, Professor D Zhao in UMD, and Prof. L Zhang in BUAA.

The authors would like to express their special thanks to China Machine Press and journal publishers. This book is an English-language version of the authors’ Chinese book (Theory and Practice: Optimal Resource Service Allocation in Manufacturing Grid by F Tao, YF Hu and L Zhang, published by China Machine Press in 2010) but with more than over 40% new content. Some of the material has been published in IEEE Transactions on Industrial Informatics(TII), International Journal of Production Research (IJPR), International Journal of Advanced Manufacturing (IJAMT), European Journal of Operational Research (EJOR), International Journal of Computer Integrated Manufacturing (IJCIM), International Journal of Manufacturing Technology and Management (IJMTM), Enterprise Information Systems(EIS), Knowledge and Information Systems (KIS), Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture. (JEM), Chinese Mechanical Engineering, and some conferences such as IEEM’2009 and INDIN’2006.

Dr. Tao would also like to express his special thanks to Professor A. Y. C. Nee for his encouragement, invaluable help and advice over the years and to his suggestion of writing this book.

Some of the book’s research and writing were made possible with the financial support of the following research projects: Excellent Doctoral Dissertation Fund from WHUT (Wuhan University of Technology), Nature Science Foundation of China (No.51005012, No.61074144 and NO.50335020), Hubei Digital Manufacturing Key Laboratory Opening Fund (No.SZ0621), Aeronautic Bairen (One Hundred Outstanding People) Plan and Weishi Youth Foundation (No.YWF-10-02-007) from Beihang University.

Thanks for Dr. H. Guo’s contribution to the Chapters 9 and 11. Dr. Tao would like to give special thanks to his master students for proofing some chapters: Y. Cheng, Y.L. Liu, and L. LV.

Thanks for the help from Martin D. Scrivener, the President of the Scrivener Publishing LLC., as well as the hard and efficient work by the other people at the publishers.

Fei Tao, Lin Zhang, Yefa Hu
Beijing, September 2011

Preface

In order to realize the goals of TQCSEFK (fastest Time-to-market, highest Quality, lowest Cost, best Service, cleanest Environment, greatest Flexibility, and high Knowledge), many advanced manufacturing technologies and models such as computer-integrated manufacturing (CIM), networked manufacturing (NM) virtual manufacturing (VM), intelligent manufacturing (IM), green manufacturing (GM), agile manufacturing (AM), concurrent engineering (CE) have been proposed. These technologies or models have played very important roles in manufacturing related fields, and have made great contributions to the development of digital manufacturing. Manufacturing grid (MGrid) is a new manufacturing model combining grid technology with the supports of some common and unified architecture, standards, and criteria computing (e.g., web service, WSDL, UDDI, SOAP, WSRF, OGSA, OGSI).

The aim of this book is to make advanced computing technologies (service computing, grid computing, and cloud computing) to be fully used in manufacturing so as to enhance the utilization and sharing of manufacturing resources, and to speed up the transformation from production-oriented manufacturing to service-oriented manufacturing. It achieves this by constructing a concrete theory for MGrid and by detailing implementation methods for MGrid resources and services. Specifically, the book:

Outline of This Book

This book consists of 15 chapters.

In Chapter 1, the motivations and driving forces of MGrid are introduced. The connotation of MGrid, including the concept, basic features, and differences between MGrid, networked manufacturing, and computing grid are investigated. The key research contents and technologies of MGrid, including its four commonly known categories and thirty one items, are also studied.

In Chapter 2, the service-oriented architecture of MGrid is proposed, as well as an MGrid collaborative manufacturing prototype platform. The resource service optimal-allocation system which supports the running of the MGrid collaborative manufacturing platform is investigated. The key functions and components for the system are described, as well as its key implementation technologies.

In Chapter 3, the issue of digital description of resource service (DDoRS) in MGrid is investigated. A method for establishing an MGrid-Ontology is presented and an MGrid-Ontology is built. A method for DDoRS based on established MGrid-Ontology is proposed. An MGrid-Ontology based MGrid resource service discovery framework is proposed.

In Chapter 4, the resource services match and search (RSMS) is studied. The describing information of resource services are classified into four categories: word concept information, sentence information, number information, and entity class (or data structure) information. The similarly matching algorithms (SMAs) for the four kinds of basic describing information are presented, including word matching algorithms (WMAs), sentence matching algorithms (SeMAs), number matching algorithms (NMAs), and entity class matching algorithms (ECMAs). Under the supports of the proposed SMAs, the process of resource services match and search are divided into four steps: basic-matching, I/O-matching, QoS-matching, and integrated-matching.

Chapter 5 studies the evaluation indices system of resource service including special evaluation indices (SEIs), individual evaluation indices (IEIs), and general quality of service (QoS) evaluation indices. The evaluation framework and method for SEIs and IEIs are studied. The modeling of MGrid QoS from the points of QoS whole-lifecycle management, MGrid architecture views, and QoS attributes parameters are investigated. A QoS-based MGrid resource service management framework is proposed.

In Chapter 6, the concept of resource service trust-QoS is presented and introduced in order to enhance the validity and success rate of MGrid resource scheduling, and provide high credible resource service abilities and results to user. The trust problems existing in the resource service transaction are put forward. The trust-QoS relationship model which is capable of capturing a comprehensive range of trust relationships exist in MGrid system is put forward. A two-layer resource service trust-QoS evaluation model (intra-domain trust-QoS and inter-domain trust-QoS evaluation models) are put forward. The quantitative evaluating algorithms of trust-QoS degree value are proposed, as well as the real-time and dynamic updating algorithms of trust-QoS degree value. A trust-QoS based MGrid resource service scheduling framework and associated realizing algorithms are proposed to illustrate the application.

In Chapter 7, an MGrid resource service optimal-selection and composition framework (MGrid-RSOSCF) is investigated. The process of resource service optimal-selection and composition is divided into five steps in MGrid-RSOSCF and the five key problems to realize MGrid-RSOSC are analyzed. The proposed MGrid-RSOSCF consists of five layers and each layer provides the corresponding necessary services and algorithms to address one problem. The five layers are: (1) T-Layer is responsible for MGrid task decomposition, (2) S-Layer is responsible for resource service match and search, (3) Q -Layer is responsible for QoS processing, (4) O-Layer is responsible for evaluating and ranking the candidate resource service, and (5) C-Layer is responsible for resource service composition and optimal-selection.

In Chapter 8, user’s feeling is taken into account in resource service optimal selection (RSOS) in MGrid system. The non-functionality QoS evaluation of resource services is based on users’ feeling and transaction experiences using intuitionistic fuzzy set (IFS). The dynamics of non-functionality QoS is considered, and a time-decay function is introduced into non-functionality QoS evaluation. A method is proposed for resource service optimal-selection based on IFS and non-functionality QoS. The performance and advantage of the proposed method are discussed.

In Chapter 9, the issue of correlation-aware composite resource service in MGrid is considered. Three kinds of correlations relationship (i.e., combinable correlation, business entity correlation, and statistical cooperate correlation) in services composition are investigated. The impact of each kinds of correlation relationship on the whole quality of resource service composition is investigated, and QoS computation model based on the three correlations is proposed. The case study indicates that the higher quality of services composition can be achieved when considering the correlations in resource services composition.

In Chapter 10, the multi-objective MGrid resource service composition and optimal-selection (MO-MRSCOS) problem is studied. The formulation is presented for an MO-MRSCOS problem with the given multi-objective (e.g., time minimization, cost minimization and reliability maximization) and multi-constraints. The basic resource service composite modes (RSCM) for composite resource service are described, and the principles for translating a complicated RSCM into a simple sequence RSCM are presented for simplifying the resolving process and complexity of MO-MRSCOS problem. A method based on the principles of particle swarm optimization (PSO), is proposed for solving MO-MRSCOS problem. Unlike previous works: (a) the proposed PSO algorithms combine the non-dominated sorting technique to achieve the selection of global best position and private best position; (b) the parameters of particle updating formulation in PSO are dynamical generated in order to make a compromise between the global exploration and local exploitation abilities of PSO; and (c) To maintain diversity of solutions in population, permutation-based and objective-based population trimming operators are applied in PSO.

In Chapter 11, the concept and the classification of resource service composition (RSC) flexibility are presented, and the measurement method of RSC flexibility is investigated to achieve the optimal-selection of RSC based on flexibility.

In Chapter 12, the resource service composition network based on complex network theory is investigated. The principles for establishing and modeling combinable relationship-based composition service network (CoRCS-Net) are studied, and nine combinable relationships among services in CoRCS-Net were investigated and fourteen elementary evolving operators for CoRCS-Net dynamic evolution are designed.

In Chapter 13, the potential failures that would generate during MGrid resource service scheduling are investigated. Thirteen failures are defined in detail, which are classified into four classifications: (a) virtual link related failures, (b) resource service related failures, (c) task related failures, and (d) application related failures. A failure management system is proposed, which provides failure-tolerance service in MGrid resource service scheduling. Corresponding detection mechanisms and methods to each defined failure are presented in detail, associated with the corresponding failure recovery methods.

In Chapter 14, the related works on the application of grid technology in manufacturing are investigated, including research on manufacturing grid (MGrid) theories and applications, and then several key future research issues of MGrid are discussed.

In Chapter 15, combing the new technologies and existing theories and technologies of current enterprise information, a computing and service-oriented manufacturing model, i.e., cloud manufacturing (CMfg), which is the future commercial realization of MGrid, is discussed based on the previous work of this book. The concept, architecture, core enabling technologies, and typical characteristics of CMfg are abstractly studied. Four typical CMfg service platforms, i.e., public, private, community, and hybrid CMfg service platforms are investigated. The key advantages and challenges for implementing CMfg are analyzed, as well as the key technologies and main research contents.

Abbreviations

AM: Agile manufacturing

App_AccessRight_Failure: Accessing right failures

App_DesignCode_Failure: Application design or coding failures

ASR: Application System Resource

Bandwidth_Failure: Bandwidth failure

BuC: Business entity correlation

CEAgent: Chief Evaluation Agent

CG: Computing grid

CIM: Computer-integrated manufacturing

C-Layer: Resource service composition layer

CMfg: Cloud manufacturing

CoR: Combinable relationship

CoRCS-Net: Combinable relationship based composition service network

CR: Computational Resource

CRS: Composite resource service

CRSS: Candidate resource service set

DDoRS: Digital description of resource service

Dep-phase: Deploy phase

Des-phase: Design phase

E&M-phase: Execution and monitor phase

E-Agent: Evaluation agent

ECA: Event–condition–action

ECMAs: Entity class matching algorithms

EEAgent: Evaluation expert agent

eiCoR: Equivalent input combinable relationship

EIS-Agent: Evaluation indices set agent

eoCoR: Equivalent output combinable relationship

eqCoR: Equivalent or competition combinable relationship

ERP: Enterprise resource planning

exCoR: Exact combinable relationship

FD: Failure detector

FR: Failure recovery

GA: Genetic algorithms

GRAM: Grid resource allocation management

IaaS: Infrastructure as a service

IEIs: Individual evaluation indices

IFS: Intuitionistic fuzzy set

IM: Intelligent manufacturing

IOPE: Inputs, outputs, preconditions, and effects

IoT: Internet of thing

irCoR: Input replaceable combinable relationship

MatchEngine: Resource service match engine

MCRS: Mixed composite resource service

MCS: Manufacturing cloud service

MCSs: Manufacturing cloud services

MDS: Miscomputing Discovery Service

MGJMS: MGrid job management system

MGrid: Manufacturing grid

MGrid-Ontology: MGrid ontology

MGrid-RSOSCF: MGrid resource service optimal-selection and composition framework

MGRS: MGrid resource service

MGRSOA: MGrid resource service optimal allocation

MO-MRSCOS: Multi-objectives MGrid resource service composition and optimal-selection

MRCM: MGrid resource conceptual model

MROM: MGrid resource objective model

MRSCOS: MGrid resource service composition and optimal-selection

MRSOAS: MGrid resource service optimal-allocation system

MRSRTask: Multi-resource service request task

NIS: Negative idea solution

NM: Networked manufacturing

NMAs: Number matching algorithms

OGSA: Open grid service architecture

OGSI: Open grid service infrastructure

O-Layer: Optimal-selection layer

orCoR: Output replaceable combinable relationship

OWL-S: Ontology web language for services

PaaS: Platform as a service

PIS: Positive idea solution

ppCoR: Partial pre-order combinable relationship

prCoR: Partial replaceable combinable relationship

psCoR: Partial successor combinable relationship

PSO: Ppartial swarm optimization

Q-Layer: QoS synthetically processing layer

QoS: Quality of Service

QoS-RSM: QoS-based resource service management

RMS: Resources Management System

RS_AbilityChange_Failure: Resource service ability changed failure

RS_Composition_Failure: Resource service composition failure

RS_Overload_Failure: Resource service overload or saturation failure

RS_Quit_Failure: Resource service quit failure

RSC-Coordinator: Resource service coordinator

RSCE-Controller: Resource service executing controller

RSC-Engine: Resource service composition engine

RSCEP: Resource service composition executing path

RSCEP-Generator: Resource service composition executing paths generator

RSCEP-Selector: Resource service composition executing paths selector

RSC-Monitor: Resource service monitor

RSD: User enterprise or resource service demander

RSIC: Rresource service information center

RS-Matcher: Resource service matcher

RSMS: Resource service match and search

RSOS: Resource service optimal-selection

RSOSC: Resource service optimal-selection and composition

RSP: Resource enterprise or resource service provider

SaaS: Software as a service

SCM: Supply chain management

SCRS: Sequence composite resource service

SEIs: Special evaluation indices

SeMAs: Sentence matching algorithms

SFN: Scale-free network

SLA: Service level agreement

S-Layer: Resource service match and search layer

SMAs: Similarity matching algorithms

SMEs: Small and medium-sized enterprises

SOAP: Simple object access protocol

SRSRTask :Single resource service request task

StC: Statistical cooperate correlation

Task_Cancel_Failure: Task cancellation failure

Task_Require_Change_Failure: Changed task requirements failure

Task_RS_Mimatch_Failure: Mismatch failure between a task and resource service

Task_Suspension_Failure: Task suspension failure

T-Layer: Task layer

TQCSEFK: Fastest Time-to-market, highest Quality, lowest Cost, best Service, cleanest Environment, greatest Flexibility, and high Knowledge

THFNMAs: Triangular fuzzy numbers matching algorithms

UDDI: Universal description, discovery and integration

VL: Virtual link

VL_Disconnect_Failure: Virtual link disconnected failure

VM: Virtual manufacturing

VO: Virtual organization

WMAs: Word matching algorithms

WSDL: Web service description language

WSRF: Web service resource framework