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Model-Driven Automated Error Recovery in Cloud Computing

Model-Driven Automated Error Recovery in Cloud Computing

Yu Sun, Jules White, Jeff Gray, Aniruddha Gokhale
Copyright: © 2011 |Pages: 20
ISBN13: 9781616928742|ISBN10: 1616928743|EISBN13: 9781616928766
DOI: 10.4018/978-1-61692-874-2.ch007
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MLA

Sun, Yu, et al. "Model-Driven Automated Error Recovery in Cloud Computing." Model-Driven Domain Analysis and Software Development: Architectures and Functions, edited by Janis Osis and Erika Asnina, IGI Global, 2011, pp. 136-155. https://doi.org/10.4018/978-1-61692-874-2.ch007

APA

Sun, Y., White, J., Gray, J., & Gokhale, A. (2011). Model-Driven Automated Error Recovery in Cloud Computing. In J. Osis & E. Asnina (Eds.), Model-Driven Domain Analysis and Software Development: Architectures and Functions (pp. 136-155). IGI Global. https://doi.org/10.4018/978-1-61692-874-2.ch007

Chicago

Sun, Yu, et al. "Model-Driven Automated Error Recovery in Cloud Computing." In Model-Driven Domain Analysis and Software Development: Architectures and Functions, edited by Janis Osis and Erika Asnina, 136-155. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-874-2.ch007

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Abstract

Cloud computing provides a platform that enables users to utilize computation, storage, and other computing resources on-demand. As the number of running nodes in the cloud increases, the potential points of failure and the complexity of recovering from error states grows correspondingly. Using the traditional cloud administrative interface to manually detect and recover from errors is tedious, time-consuming, and error prone. This chapter presents an innovative approach to automate cloud error detection and recovery based on a run-time model that monitors and manages the running nodes in a cloud. When administrators identify and correct errors in the model, an inference engine is used to identify the specific state pattern in the model to which they were reacting, and to record their recovery actions. An error detection and recovery pattern can be generated from the inference and applied automatically whenever the same error occurs again.

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