Full metadata
Title
Towards effective and intelligent multi-tenancy SaaS
Description
Cloud computing has received significant attention recently as it is a new computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and visualized manner. SaaS (Software-as-a-Service) provide a now paradigm in cloud computing, which goal is to provide an effective and intelligent way to support end users' on-demand requirements to computing resources, including maturity levels of customizable, multi-tenancy and scalability. To meet requirements of on-demand, my thesis discusses several critical research problems and proposed solutions using real application scenarios. Service providers receive multiple requests from customers, how to prioritize those service requests to maximize the business values is one of the most important issues in cloud. An innovative prioritization model is proposed, which uses different types of information, including customer, service, environment and workflow information to optimize the performance of the system. To provide "on-demand" services, an accurate demand prediction and provision become critical for the successful of the cloud computing. An effective demand prediction model is proposed, and applied to a real mortgage application. To support SaaS customization and fulfill the various functional and quality requirements of individual tenants, a unified and innovative multi-layered customization framework is proposed to support and manage the variability of SaaS applications. To support scalable SaaS, a hybrid database design to support SaaS customization with two-layer database partitioning is proposed. To support secure SaaS, O-RBAC, an ontology based RBAC (Role based Access Control) model is used for Multi-Tenancy Architecture in clouds. To support a significant number of tenants, an easy to use SaaS construction framework is proposed. As a summary, this thesis discusses the most important research problems in cloud computing, towards effective and intelligent SaaS. The research in this thesis is critical to the development of cloud computing and provides fundamental solutions to those problems.
Date Created
2011
Contributors
- Shao, Qihong (Author)
- Tsai, Wei-Tek (Thesis advisor)
- Askin, Ronald (Committee member)
- Ye, Jieping (Committee member)
- Naphade, Milind (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xviii, 285 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.8834
Statement of Responsibility
by Qihong Shao
Description Source
Viewed on Sept. 25, 2012
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2011
bibliography
Includes bibliographical references (p. 270-284)
Field of study: Computer science
System Created
- 2011-08-12 03:26:03
System Modified
- 2021-08-30 01:55:40
- 3 years 1 month ago
Additional Formats