# Turning Video Resource Management into Cloud Computing
## 1 Introduction  
With the development of information technologies, tremendous challenges have emerged in video resource management. Transferring this work into online service systems is a feasible solution for resolving the problems. Video resources with huge amounts of volumes and complex data types are a kind of typical big data, and are difficult for processing. Cloud services have been regarded as a significant trend of technical industries and applications after the Web services era. Generally, the framework of cloud services consists of infrastructures, operating systems, virtual machines, storages, and cloud web application services. With the improvement of global network performance over the past few years, research on sharing video resources with large volumes and complex data types online based on cloud computing has attracted more and more attentions [1]. Video processing is a notably data-intensive, time-consuming, and computing-intensive application. Upfront infrastructure investment is usually costly, especially when dealing with applications where time-to-market is a crucial requirement [2]. Traditionally, a great volume of video resources is generated, stored, and managed on local servers daily. That obviously incurs some problems in keeping daily video resources in data centers, such as limitations of bandwidth, storage space, overloading, reliability, and scalability [3]. When the number of users online reaches a certain scale, the limitation of bandwidth will greatly influence the accessing speeds of response servers. Meanwhile, data servers need to deal with the loading balances between huge volumes of video resources and users, and bring buffer a phenomenon. This paper proposes a novel architecture based on well-developed peer-to-peer (P2P) technology and emerging cloud computing aiming to solve these issues. The architecture exploits inherent characteristics of P2P and cloud computing to provide an economic, scalable, reliable, and efficient model to manage video resources. This paper focused on architecture, components, operation flows, and implementation of video resource management systems. Video resources alwaysbeing unavailable to users due to their huge volume makes the access speed slow and overloads the storage media. The emergence of cloud computing provides a new solution to efficiently manage video resources.  
Hadoop is an open-source software framework that supports data-intensive distributed applications, running off applications on large clusters of commodity hardware. It also transparently provides both reliability and data motion to applications. Hadoop implements a computational paradigm named Map/Reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in computing clusters. In addition, it provides a distributed file system that stores data across data nodes with very high aggregate bandwidth. Both Map/Reduce and distributed file systems are designed to automatically handle problems of node failures. It enables applications to work with thousands of computation-independent computers and petabytes of data.