Abstract
In this paper we discuss the assurance of self-adaptive controllers for the Cloud, and we propose a taxonomy of controllers based on the supported assurance level. Self-adaptive systems for the Cloud are commonly built by means of controllers that aim to guarantee the required quality of service while containing costs, through a careful allocation of resources. Controllers determine the allocation of resources at runtime, based on the inputs and the status of the system, and referring to some knowledge, usually represented as adaptation rules or models. Assuring the reliability of self-adaptive controllers account to assuring that the adaptation rules or models represent well the system evolution. In this paper, we identify different categories of control models based on the assurance approaches. We introduce two main dimensions that characterize control models. The dimensions refer to the flexibility and scope of the system adaptability, and to the accuracy of the assurance results. We group control models in three main classes that depend on the kind of supported assurance that may be checked either at design or runtime. Controllers that support assurance of the control models at design time privilege reliability over adaptability. They usually represent the system at a high granularity level and come with high costs. Controllers that support assurance of the control models at runtime privilege adaptability over reliability. They represent the system at a finer granularity level and come with reduced costs. Controllers that combine different models may balance verification at design and runtime and find a good trade off between reliability, adaptability, granularity and costs.
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Gambi, A., Toffetti, G., Pezzè, M. (2013). Assurance of Self-adaptive Controllers for the Cloud. In: Cámara, J., de Lemos, R., Ghezzi, C., Lopes, A. (eds) Assurances for Self-Adaptive Systems. Lecture Notes in Computer Science, vol 7740. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36249-1_12
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DOI: https://doi.org/10.1007/978-3-642-36249-1_12
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