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MapReduce is a parallel programming system to deal with massive data. It can automatically parallelize MapReduce jobs into multiple tasks, schedule to a cluster built by PCs. This paper describes a data distribution aware MapReduce task scheduling strategy. When worker nodes requests for tasks, it will compute and obtain nodes΄ priority according to the times for request, the number of tasks which can be executed locally and so on. Meanwhile, it can also calculate tasks΄ priority according to the numbers of copies executed by the task, latency time of tasks and so on. This strategy is based on node and task΄s scheduling priority, fully considers data distribution in the system and thus schedules Map tasks to nodes having data in high probability, to reduce network overhead and improve system efficiency. [ABSTRACT FROM AUTHOR]

In order to support the maximum number of user and elastic service with the minimum resource, the Internet service provider invented the cloud computing. within a few years, emerging cloud computing has became the hottest technology. From the publication of core papers by Google since 2003 to the commercialization of Amazon EC2 in 2006, and to the service offering of AT&T Synaptic Hosting, the cloud computing has been evolved from internal IT system to public service, from cost-saving tools to revenue generator, and from ISP to telecom. This paper introduces the concept, history, pros and cons of cloud computing as well as the value chain and standardization effort. [ABSTRACT FROM AUTHOR]