Atjaunināt sīkdatņu piekrišanu

Design of Cloud Workflow Systems 2012 [Mīkstie vāki]

  • Formāts: Paperback / softback, 97 pages, height x width: 235x155 mm, weight: 454 g, 33 Illustrations, black and white; XIV, 97 p. 33 illus., 1 Paperback / softback
  • Sērija : SpringerBriefs in Computer Science
  • Izdošanas datums: 01-Nov-2011
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1461419328
  • ISBN-13: 9781461419327
  • Mīkstie vāki
  • Cena: 46,91 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 55,19 €
  • Ietaupiet 15%
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Formāts: Paperback / softback, 97 pages, height x width: 235x155 mm, weight: 454 g, 33 Illustrations, black and white; XIV, 97 p. 33 illus., 1 Paperback / softback
  • Sērija : SpringerBriefs in Computer Science
  • Izdošanas datums: 01-Nov-2011
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1461419328
  • ISBN-13: 9781461419327

Cloud computing is the latest market-oriented computing paradigm which brings software design and development into a new era characterized by “XaaS”, i.e. everything as a service. Cloud workflows, as typical software applications in the cloud, are composed of a set of partially ordered cloud software services to achieve specific goals. However, due to the low QoS (quality of service) nature of the cloud environment, the design of workflow systems in the cloud becomes a challenging issue for the delivery of high quality cloud workflow applications. To address such an issue, this book presents a systematic investigation to the three critical aspects for the design of a cloud workflow system, viz. system architecture, system functionality and quality of service. Specifically, the system architecture for a cloud workflow system is designed based on the general four-layer cloud architecture, viz. application layer, platform layer, unified resources layer and fabric layer. The system functionality for a cloud workflow system is designed based on the general workflow reference model but with significant extensions to accommodate software services in the cloud. The support of QoS is critical for the quality of cloud workflow applications. This book presents a generic framework to facilitate a unified design and development process for software components that deliver lifecycle support for different QoS requirements. While the general QoS requirements for cloud workflow applications can have many dimensions, this book mainly focuses on three of the most important ones, viz. performance, reliability and security. In this book, the architecture, functionality and QoS management of our SwinDeW-C prototype cloud workflow system are demonstrated in detail as a case study to evaluate our generic design for cloud workflow systems. To conclude, this book offers a general overview of cloud workflow systems and provides comprehensive introductions to the design of the system architecture, system functionality and QoS management.

Recenzijas

From the reviews:

This short, well-written book presents a comprehensive treatment of workflow systems for cloud computing. The book is organized into five chapters and five appendices, plus a bibliography. The bibliography lists 92 works that deal with aspects of cloud computing and workflows. This easy-to-read book gives a good foundation for the topics addressed. (Anoop Malaviya, ACM Computing Reviews, October, 2012)

1 Workflow Systems in the Cloud
1(12)
1.1 Background: Cloud Computing
1(2)
1.2 Background: Workflow Systems
3(1)
1.3 Cloud Workflow Systems
4(2)
1.4 Motivating Examples
6(5)
1.5 Key Issues in the Design of Cloud Workflow Systems
11(2)
2 Cloud Workflow System Architecture
13(6)
2.1 General Cloud Software Architecture
13(3)
2.1.1 Cloud Architecture
13(1)
2.1.2 Example: Aneka Cloud Architecture
14(2)
2.2 General Architecture of Cloud Workflow Systems
16(3)
2.2.1 Cloud Workflow System Architecture
16(1)
2.2.2 Example: Window Workflow Foundation Architecture
17(2)
3 Cloud Workflow System Functionality
19(8)
3.1 Classical Workflow Reference Model
19(3)
3.2 Basic Functionalities of Cloud Workflow Systems
22(5)
3.2.1 Cloud Workflow System Functionality
22(2)
3.2.2 Example: Kepler Web/Grid Service Management
24(1)
3.2.3 Example: CloudBus Cloud Resource Management
25(2)
4 Cloud Workflow System Quality of Service
27(24)
4.1 QoS of Cloud Services and Web Services
27(4)
4.1.1 General QoS
27(2)
4.1.2 SLA Management
29(2)
4.2 QoS of Cloud/Grid Workflows
31(2)
4.3 A Generic QoS Framework
33(4)
4.4 Example 1: Time Management (on Temporal Constraints)
37(2)
4.5 Example 2: Cost Management (on Data Storage)
39(5)
4.5.1 Cost Model of Datasets Storage in the Cloud
40(2)
4.5.2 Minimum Cost Benchmarking of Datasets Storage in the Cloud
42(1)
4.5.3 Cost-Effective Datasets Storage Strategies
42(2)
4.6 Example 3: Reliability Management (on Data Replication)
44(3)
4.6.1 Data Replication
45(1)
4.6.2 Data Storage Reliability Model
46(1)
4.6.3 Cost-Effective Incremental Replication Strategy
47(1)
4.7 Example 4: Security Management (on Privacy)
47(4)
4.7.1 Privacy Protection in Cloud
48(1)
4.7.2 Trust Based Privacy Protection
49(2)
5 Case Study: SwinDeW-C Cloud Workflow System
51(18)
5.1 Overview of SwinDeW-G Environment
51(2)
5.2 SwinDeW-C System Architecture
53(5)
5.2.1 SwinCloud Infrastructure
54(1)
5.2.2 Architecture of SwinDeW-C
54(3)
5.2.3 Functionalities of SwinDeW-C Peers
57(1)
5.3 QoS Management Components in SwinDeW-C
58(5)
5.3.1 Performance Management in SwinDeW-C
58(2)
5.3.2 Data Management (Storage and Replication) in SwinDeW-C
60(1)
5.3.3 Security Management in SwinDeW-C
61(2)
5.4 SwinDeW-C System Prototype
63(1)
5.5 Experiments
64(5)
5.5.1 Evaluation on Performance Management
64(2)
5.5.2 Evaluation on Data Storage Management
66(3)
Appendix A Performance Management Strategies 69(8)
Appendix B Data Storage Management Strategies 77(6)
Appendix C Replication Management Strategies 83(2)
Appendix D Trust-Based Noise Injection Strategy 85(4)
Appendix E Literature Review 89(4)
Bibliography 93
Xiao Liu received his PhD degree in Computer Science and Software Engineering from the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia in 2011. He received his Master and Bachelor degree from the School of Management, Hefei University of Technology, Hefei, China, in 2007 and 2004 respectively, all in Information Management and Information System. He is currently a postdoctoral research fellow in the Centre of Computing and Engineering Software System at Swinburne University of Technology. His research interests include workflow management systems, scientific workflow, business process management and quality of service. Dong Yuan received the Bachelor degree in 2005 and Master degree in 2008 both from Shandong University, Jinan, China, all in Computer Science. He is currently a PhD student in the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia. His research interests include data management in workflow systems, scheduling and resource management, grid and cloud computing.

Gaofeng Zhang received the Bachelor and Master degrees in Computer Science from Hefei University of Technology, Hefei, China, in 2005 and 2008 respectively. He is currently working toward the PhD degree in Information and Communication Technology under the supervision of A/Prof. Jinjun Chen and Prof. Yun Yang in Faulty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia. His research interests include privacy protection strategy, risk evaluation, and security mechanism in cloud computing.

Wenhao Li obtained his Bachelor and Master degree of Engineering from Shan Dong University in China in 2007 and 2010 respectively. He participated in a program funds by National Natural Science Foundation of China during his postgraduate studies and published several papers in national and international journals. He is currently a first-year PhD candidate in Faculty of Information and Communication Technologies, Swinburne University of Technology, supervised by Prof. Yun Yang and A/Prof. Jinjun Chen. Hes research interests include parallel and distributed computing, cloud and grid computing, workflow technologies and data management in distributed computing environment.

Dahai Cao received his master degree in software engineering from Tsinghua University, Beijing, China, 2005. He is currently a PhD student in Swinburne University Centre for Computing and Engineering Software Systems, Melbourne, Australia. His research interests include cloud-based large-scale workflow management systems, adaptive workflow management and cloud computing. received his master degree in software engineering from Tsinghua University, Beijing, China, 2005. He is currently a PhD student in Swinburne University Centre for Computing and Engineering Software Systems, Melbourne, Australia. His research interests include cloud-based large-scale workflow management systems, adaptive workflow management and cloud computing.

Qiang He received his first Ph. D. degree in information and communication technology from Swinburne University of Technology (SUT), Australia, in 2009 and his second Ph. D. degree in computer science and engineering from Huazhong University of Science and Technology (HUST), China, in 2010. He is now a research fellow at SUT. His research interests include services computing, cloud computing, P2P system, workflow management and agent technologies.

Jinjun Chen received his PhD degree in Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2007. He is currently an associate Professor in the Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include Scientific workflow management andapplications, workflow management and applications in Web service or SOC environments, workflow management and applications in grid (service)/cloud computing environments, software verification and validation in workflow systems, QoS and resource scheduling in distributed computing systems such as cloud computing, service oriented computing, semantics and knowledge management, cloud computing.

Yun Yang received a Master of Engineering degree from The University of Science and Technology of China, Hefei, China, in 1987, and a PhD degree from The University of Queensland, Brisbane, Australia, in 1992, all in computer science. He is currently a full Professor in the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne as an Associate Professor in late 1999, he was a Lecture and Senior Lecturer at Deakin University during 1996-1999. Before that, he was a Research Scientist at DSTC - Cooperative Research Centre for Distributed Systems Technology during 1993-1996. He also worked at Beihang University in China during 1987-1988. He has published more about 200 papers on journals and refereed conferences. His research interests include software engineering; p2p, grid and cloud computing; workflow systems; service-oriented computing; Internet computing applications; and CSCW.