Dynamic resource provisioning and secured file sharing using virtualization in cloud azure

Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for reduced overhead with multiple systems running from the same console at the same time. A physical server is a bare-metal system whose hardware is controlled by the host operating system. A physical server runs on a single instance of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed, for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script. Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application. The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure. The research work was carried out in a secure way through the migration process from P2V2C.Web of Science111art. no. 4

Data Replication and Its Alignment with Fault Management in the Cloud Environment

Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time