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Durability indicates a system's ability to preserve committed data in the long term. It is one of the key requirements for storage systems. Various fault tolerance strategies have been proposed for traditional distributed databases, peer-to-peer (P2P) systems, and grid systems. These strategies were all developed for specific platforms and application types and have been tailored to the characteristics of the underlying system architectures and application requirements. Cloud systems differ from these previous frameworks in that they are designed to support large numbers of customer-oriented applications, each with different quality of service (QoS) requirements and resource consumption characteristics. Moreover, most cloud architectures are deployed over large-scale geographically distributed infrastructures, raising challenges on data durability as well as system efficiency and scalability. In this paper, we investigated different approaches to the design of durable data cloud platforms. In particular, we consider prevailing cloud platforms and cloud-based applications and examine the impacts of different redundancy and repair strategies to enhance the durability of the data in the cloud. We verify the performance of various approaches to data durability in the Cloud through extensive simulations.