Disaster recovery in cloud computing: a survey <s> Disaster Recovery Plan <s> Making copies of large sets of data is a common activity. These copies can provide a consistent image for a backup, a checkpoint for restoring the state of an application, a source for data mining, real data to test a new version of an application, and so on. One characteristic all of these uses have in common is that it is important that the copy appear to occur atomically, i.e., any updates to the data source being copied either occur before or after the copy. In this work, we examine the history, the state-of-the art, and possible future of mechanisms for copying large quantities of data via storage subsystem facilities for providing point-in-time (PiT) copies. <s> BIB001 </s> Disaster recovery in cloud computing: a survey <s> Disaster Recovery Plan <s> The need for high availability (HA) and disaster recovery (DR) in IT environment is more stringent than most of the other sectors of enterprises. Many businesses require the availability of business-critical applications 24 hours a day, seven days a week, and can afford no data loss in the event of a disaster. It is vital that the IT infrastructure is resilient with regard to disruption, even site failures, and that business operations can continue without significant impact. As a result, DR has gained great importance in IT. Clustering of multiple industries standard servers together to allow workload sharing and fail-over capabilities is a low cost approach. In this paper, we present the availability model through Semi-Markov Process (SMP) and also analyze the difference in downtime of the SMP model and the approximate Continuous Time Markov Chain (CTMC) model. To acquire system availability, we perform numerical analysis and SHARPE tool evaluation. <s> BIB002 </s> Disaster recovery in cloud computing: a survey <s> Disaster Recovery Plan <s> We present the design and implementation of ENDEAVOUR - a framework for integrated end-to-end disaster recovery (DR) planning. Unlike existing research that provides DR planning within a single layer of the IT stack (e.g. storage controller based replication), ENDEAVOUR can choose technologies and composition of technologies across multiple layers like virtual machines, databases and storage controllers. ENDEAVOUR uses a canonical model of available replication technologies at all layers, explores strategies to compose them, and performs a novel map-search-reduce heuristic to identify the best DR plans for given administrator requirements. We present a detailed analysis of ENDEAVOUR including empirical characterization of various DR technologies, their composition, and a end-to-end case study. <s> BIB003 </s> Disaster recovery in cloud computing: a survey <s> Disaster Recovery Plan <s> Currently, organizations are increasingly aware of the need to protect their computer infrastructure to maintain continuity of operations. This process involves a number of different concerns including: managing natural disasters, equipment failure, and security breaches, poor data management, inadequate design, and complex/impractical design. The purpose of this article is to delineate how virtualization of hosts and cloud computing can be used to address the concerns resulting in improved computer infrastructure that can easily be restored following a natural disaster and which features fault tolerant hosts/components, isolates applications security attacks, is simpler in design, and is easier to manage. Further, because this technology has been out for a number of years and its capabilities have matured an attempt has been made to describe those capabilities as well as document successful applications. <s> BIB004
There are different DR approaches to develop a recovery plan in cloud system. They are based on the nature of the system. However in the literature, all these approaches are based on redundancy and backup strategies. The redundancy strategy uses separated parallel sites which have the ability to start up the applications after a disaster; whereas backup strategy uses replication technology BIB002 ). The speed and protection degree of these approaches depend on the level of DR service that is shown in Table 5 BIB004 The objective of disaster recovery planning is to minimize RTO, RPO, cost, and latency by considering system constraints such as CPU, network and storage requirements. So we can say DR recovery planning can be considered as an optimization problem. According to BIB003 , DR plans include two necessary phases:  Matching Phase: In this phase, all DR solutions have to be matched to the requirements of any data container (a data container means a data set with identical DR requirements)  Plan composition phase: Selecting an optimal DR solution which can minimize cost with respect to required QoS for each data container. ENDEAVOUR BIB003 ) is a framework for DR planning process. As shown in Figure 2 , it consists three modules:  Input modules: Including DR requirements (such as protection type, RTO, RPO and application latency), Discovery engine (To find configuration information of primary and secondary sites) and knowledge repository (Replication technologies, instructions and composition formula).  Planning Modules: Including solution generation (Analyzing DR requirements and matching them to replication techniques), Ranking (Sorting DR plans in terms of some attributes like cost, risk and latency BIB001 ) and Global optimization (selecting an optimal DR plan  ).  Output: The output of ENDEAVOUR is an optimal DR plan for each application with some details like: target resources and devices, replication protocol configuration.