Cloud computing is the latest effort in delivering computing resources as a service to small and medium sized enterprises. These enterprise organizations require installing and maintaining expensive equipment to keep business up and running at all the times. Naturally this requires building an infrastructure flexible enough to respond to any threat under all circumstances. Any disaster may be considered to be a threat associated with the IT infrastructure in a data center. Disaster can occur either naturally or by humans. This paper is focused on how disaster may be controlled in a cloud computing data center which provides services to an organization and how to keep the organization business running while a disaster strikes. The availability and performance of any service is measured by its overall uptime. Recent recovery techniques that have been developed in cloud computing domain have several advantages and disadvantages. Therefore, researchers should conduct some investigations in this field. A hybrid service which utilize redundancy and fault tolerance techniques for providing more accurate recovery in cloud computing when disaster strikes is proposed in order to overcome these challenges in this paper. This hybrid service integrates the Infrastructure as a Service (IaaS) and Disaster Recovery as another Service (DRaaS). The proposed framework is formed by the integration of five essential types of proven redundancy techniques that have a major impact on the uptime of the services during disaster in cloud data centers. For evaluation of the proposed framework, a survey was conducted through a questionnaire presented to and filled by networking professionals and experts. The outcome of data analysis indicates that redundancy-based disaster recovery framework improves the performance of data center recovery and results in a high level of availability of the restored enterprise when disaster strikes. A total of 59.4 % of survey respondents accepted the fact that this framework reduces more than 70 % of threats associated with disaster.
|Number of pages||10|
|Journal||Journal of Soft Computing and Decision Support Systems|
|Publication status||Published - 2017|