Development and Instrumentation of a Framework for the Generation and Management of Self-Adaptive Enterprise Applications
Companies’ operations have become over-dependent on their supporting enterprise software applications. This situation has placed a heavy burden onto software maintenance teams who are expected to keep these applications up and running optimally in varying execution conditions. However, this high hum...
| Autor Principal: | Arboleda, Hugo |
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| Otros Autores: | Paz, Andrés, Jiménez, Miguel, Tamura, Gabriel |
| Formato: | info:eu-repo/semantics/article |
| Idioma: | eng |
| Publicado: |
Pontificia Universidad Javeriana
2016
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| Acceso en línea: |
http://revistas.javeriana.edu.co/index.php/iyu/article/view/15215 |
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| Sumario: |
Companies’ operations have become over-dependent on their supporting enterprise software applications. This situation has placed a heavy burden onto software maintenance teams who are expected to keep these applications up and running optimally in varying execution conditions. However, this high human intervention drives up the overall costs of software ownership. In addition, the current dynamic nature of enterprise applications constitutes challenges with respect to their architectural design and development, and the guarantee of the agreed quality requirements at runtime. Efficiently and effectively achieving the adaptation of enterprise applications requires an autonomic solution. In this paper we present SHIFT, a framework that provides (i) facilities and mechanisms for managing self-adaptive enterprise applications through the use of an autonomic infrastructure, and (ii) automated derivation of self-adaptive enterprise applications and their respective monitoring infrastructure. Along with the framework, our work leads us to propose a reference specification and architectural design for implementing self-adaptation autonomic infrastructures. We developed a reference implementation of SHIFT; our contribution includes the development of monitoring infrastructures, and dynamic adaptation planning and automated derivation strategies. |
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