Data backup and recovery with a minimum replica plan in a multi-cloud environment

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replicationsin the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial ofservice attacksto maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicasreplication strategies

Towards Energy-Efficient, Fault-Tolerant, and Load-Balanced Mobile Cloud

Recent advances in mobile technologies have enabled a new computing paradigm in which large amounts of data are generated and accessed from mobile devices. However, running resource-intensive applications (e.g., video/image storage and processing or map-reduce type) on a single mobile device still remains off bounds since it requires large computation and storage capabilities. Computer scientists overcome this issue by exploiting the abundant computation and storage resources from traditional cloud to enhance the capabilities of end-user mobile devices. Nevertheless, the designs that rely on remote cloud services sometimes underlook the available resources (e.g., storage, communication, and processing) on mobile devices. In particular, when the remote cloud services are unavailable (due to service provider or network issues) these smart devices become unusable. For mobile devices deployed in an infrastructureless network where nodes can move, join, or leave the network dynamically, the challenges on energy-efficiency, reliability, and load-balance are still largely unexplored. This research investigates challenges and proposes solutions for deploying mobile application in such environments. In particular, we focus on a distributed data storage and data processing framework for mobile cloud. The proposed mobile cloud computing (MCC) framework provides data storage and data processing services to MCC applications such as video storage and processing or map-reduce type. These services ensure the mobile cloud is energy-efficient, fault-tolerant, and load-balanced by intelligently allocating and managing the stored data and processing tasks accounting for the limited resources on mobile devices. When considering the load-balance, the framework also incorporates the heterogeneous characteristics of mobile cloud in which nodes may have various energy, communication, and processing capabilities. All the designs are built on the k-out-of-n computing theoretical foundation. The novel formulations produce a reliability-compliant, energy-efficient data storage solution and a deadline-compliant, energy-efficient job scheduler. From the promising outcomes of this research, a future where mobile cloud offers real-time computation capabilities in complex environments such as disaster relief or warzone is certainly not far

GREEDY SINGLE USER AND FAIR MULTIPLE USERS REPLICA SELECTION DECISION IN DATA GRID

Replication in data grids increases data availability, accessibility and reliability. Replicas of datasets are usually distributed to different sites, and the choice of any replica locations has a significant impact. Replica selection algorithms decide the best replica places based on some criteria. To this end, a family of efficient replica selection systems has been proposed (RsDGrid). The problem presented in this thesis is how to select the best replica location that achieve less time, higher QoS, consistency with users' preferences and almost equal users' satisfactions. RsDGrid consists of three systems: A-system, D-system, and M-system. Each of them has its own scope and specifications. RsDGrid switches among these systems according to the decision maker