Model-Driven Automated Error Recovery in Cloud Computing

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Author(s): Yu Sun (University of Alabama at Birmingham, USA), Jules White (Virginia Tech, USA), Jeff Gray (University of Alabama, USA)and Aniruddha Gokhale (Vanderbilt University, USA)
Copyright: 2011
Pages: 20
Source title: Model-Driven Domain Analysis and Software Development: Architectures and Functions
Source Author(s)/Editor(s): Janis Osis (Riga Technical University, Latvia)and Erika Asnina (Riga Technical University, Latvia)
DOI: 10.4018/978-1-61692-874-2.ch007

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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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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