With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirement. Numerous power data would be accumulated to even Terabyte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single record query, these software modules: Hive, Spark, and Impala had been tested in terms of query-response efficiency. And for the performance exploration on the statistical query function and data ETL processing. The kernel contributions of this research work can be highlighted by two parts: (1) Multi-layer software modules are adopted to design and implement the real-time power-monitoring platform embedded with some excellent characteristics of high efficiency, high feasibility and low cost. (2) The rudimental experiments are conducted to verify the query-response efficiency, and performance evaluations for the proposed real-time power monitoring platform, which reveals the high feasibility for the target research goals.

Cloud computing refers to both the applications delivered as services over the Internet and hardware and system software in the cloud server farm that provides those services. The research on server backup of cloud server farm appears to be an important issue of cloud computing economics. Optimal logistic policy should be considered to be a profit-oriented framework simultaneously for providing qualified service to cloud users while the whole cloud center is under construction. The kernel point of the proposed approach is that a novel design pattern is developed for approaching optimal profit on logistics using the finite-source queuing theory. To model the proposed approach for qualitative analysis, a Petri Net model was developed to configure all relevant system aspects in a concise fashion. On quantitative work, a comprehensive mathematical analysis on profit pattern has been made in detail. Relevant simulations have also been conducted tovalidate the proposed optimization model. The design illustration is presented to demonstrate engineering application scenario in cloud environment, hence the proposed approach indeed provides a feasibly profit-oriented framework to meet logistic economy.

In this work, we particularly focus on the use of free open-source software, so that end users do not need to spend a huge amount of software license fees. For cloud computing, virtualization technology delivers numerous benefits in addition to being one of the basic roles to build a cloud environment. By virtualization, enterprises can maximize working efficiency without the need to install more facilities in the computer room. In this study, we implemented a virtualization environment and performed experiments on it. The main subject of it is how to use the Open Stack open-source software to build a cloud infrastructure with high availability and a dynamic resource allocation mechanism. It provides a private cloud solution for business and organizations. It belongs to Infrastructure as a Service (IaaS), one of the three service models in the cloud. For the part of the user interface, a web interface was used to reduce the complexity of access to cloud resources for users. We measured the performance of live migration of virtual machines with different specifications and analyzed the data. Also according to live migration modes, we wrote an algorithm to solve the traditional migration problem that needs manually determining whether the machine load is too heavy or not, as a result, the virtual machine load level is automatically detected, and the purpose of automatic dynamic migration to balance resources of servers is achieved.

With the ever-increasing popularity of the cloud platform, cloud backup scheme attracts more attention from both industry and academia. For cloud providers, profit evaluation of server farms is an important issue of cloud computing economics. Optimal logistic policy should be considered to be an indispensible design simultaneously for providing qualified service to cloud users while the whole cloud center is under construction. To maintain contract-based service quality, the administrator of server farm should adopt the necessary redundancy strategy to provide backup servers when some servers fail. To this aim, focusing upon exploring the optimal profit, two decision parameters of spares and repairmen in the system are considered to maintain regulated service quality for the cloud users. The basic point of our approach is that a novel design approach is developed for evaluating the profit patterns using the finite-source queuing theory. A comprehensive mathematical analysis on profit pattern has been made in detail. Numerical simulation has also been conducted to validate the proposed optimization model. The design illustration is presented to demonstrate engineering application scenario in cloud computing environment, hence the proposed approach indeed provides a feasibly cost-effective design framework to meet logistic economy.

This study elaborates the influence patterns of different backup strategies on AODV-based routing protocols. Although some backup routing strategies yield good data delivery rates, they suffer from low efficiency. To make the process of data salvation more efficiently in case of link failure, we explore the possibility of combining the AODV-BR strategy and on-demand node-disjoint multi-path routing protocols. This article proposes an improved approach named NDAMR (node-disjoint alternative multiple path), a routing protocol that maintains the only two shortest backup paths in the source and destination nodes. The NDAMR can alleviate the redundancy-frames overhead during the process of data salvation by the neighboring intermediate nodes. Our simulation results have demonstrated that NDAMR delivers good data delivery performance while restricting the impacts of transmission collision and contention.