
Zhen Ye- The University of Queensland
Zhen Ye
- The University of Queensland
About
6
Publications
2,785
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
378
Citations
Introduction
Skills and Expertise
Current institution
Publications
Publications (6)
This article considers cloud service composition from a decision analysis perspective. Traditional QoS-aware composition techniques usually consider the qualities available at the time of the composition because compositions are usually immediately consumed. This is fundamentally different in the cloud environment where the cloud service compositio...
We propose a new framework for composing Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate spatio-temporal Sensor-Cloud services, two new quality attributes are introduced. We present a heuristic algorithm based on A∗ to compose Sensor-Cloud services in terms of spatio-temporal aspects. In addition, a new...
We propose a cloud service composition framework that selects the optimal composition based on an end user’s long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multiva...
Cloud service composition is usually long term based and economically driven. We propose to use multi-dimensional Time Series to represent the economic models during composition. Cloud service composition problem is then modeled as a similarity search problem. Next, a novel correlation-based search algorithm is proposed. Finally, experiments and th...
Cloud service composition is usually long term based and economically driven. We consider cloud service composition from a user-based perspective. Specifically, the contributions are shown in three aspects. We propose to use discrete Bayesian Network to represent the economic model of end users. The cloud service composition problem is modeled as a...
Services in cloud computing can be categorized into two groups: Application services and Utility Computing Services. Compositions in the application level are similar to the Web service compositions in SOC (Service-Oriented Computing). Compositions
in the utility level are similar to the task matching and scheduling in grid computing. Contributions...