IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

CATS-CAE Reflective Middleware Framework for Adapting Context-Aware Transactional Services: Using a Hybrid Policy-Based Approach

CATS-CAE Reflective Middleware Framework for Adapting Context-Aware Transactional Services: Using a Hybrid Policy-Based Approach
View Sample PDF
Author(s): Widad Ettazi (IMS Team, ADMIR Laboratory, ENSIAS, Rabat IT Center, Mohammed V University, Rabat, Morocco), Mahmoud Nassar (IMS Team, ADMIR Laboratory, ENSIAS, Rabat IT Center, Mohammed V University, Rabat, Morocco)and Hatim Hafiddi (SEEDS Team, STRS Laboratory, INPT, Rabat, Morocco)
Copyright: 2020
Volume: 17
Issue: 2
Pages: 19
Source title: International Journal of Web Services Research (IJWSR)
Editor(s)-in-Chief: Liang-Jie Zhang (Kingdee International Software Group, China)and Chia-Wen Tsai (Ming Chuan University, Taiwan)
DOI: 10.4018/IJWSR.2020040103

Purchase


Abstract

Pervasive environments are characterized by limited computing resources and wireless connectivity. In parallel, current application domains have variable transactional requirements that do not fit the traditional ACID model. As a result, the pervasive environment characteristics are compelling and cannot be supported by conventional solutions that are typically dedicated to a specific application domain and support a limited set of context parameters. This article aims at providing a complete solution that addresses the challenges of the adaptability of context-aware transactional services “CATS” in pervasive environments. Thus, a new framework CATS-CAE was designed, which offers a comprehensive structure of multiple component chains. The adaptation strategy in CATS-CAE is based on a hybrid approach combining the use of adaptation policies, alternative strategy and behavioral adaptation of composite services through the “Profiled Task Class” concept. A probabilistic model is also presented to support the efficiency of the proposed approach.

Related Content

Jinping Zhang. © 2024. 17 pages.
Ahmad Radwan, Mohannad Amarneh, Hussam Alawneh, Huthaifa I. Ashqar, Anas AlSobeh, Aws Abed Al Raheem Magableh. © 2024. 22 pages.
Zhuolin Mei, Huilai Zou, Jinzhou Huang, Caicai Zhang, Bin Wu, Jiaoli Shi, Zhengxiang Cheng. © 2024. 17 pages.
Shouning Huang. © 2024. 18 pages.
Xiang Xie, Jianxun Liu, Buqing Cao, Mi Peng, Guosheng Kang, Yiping Wen, Kenneth K. Fletcher. © 2023. 17 pages.
Yunfei Li, Shichao Yin. © 2023. 17 pages.
Yong Lu, Ming Zhe Jin. © 2023. 14 pages.
Body Bottom