Currently, the data to be explored and exploited by computing systems increases at an
exponential rate. The massive amount of data or so-called “Big Data” put pressure on …

Big Data (BD) platforms have a long tradition of leveraging trends and technologies from the
broader computer network and communication community. For several years, dedicated …

In scientific cloud workflows, large amounts of application data need to be stored in
distributed data centres. To effectively store these data, a data manager must intelligently …

MapReduce offers an ease-of-use programming paradigm for processing large data sets,
making it an attractive model for distributed volunteer computing systems. However, unlike …

Cloud computing is a promising distributed computing platform for big data applications, eg,
scientific applications, since excessive resources can be obtained from cloud services for …

In cloud computing, data placement is a critical operation performed as part of workflow
management and aims to find the best physical machine to place the data. It has direct …

Desktop Grids use the computing, network and storage resources from idle desktop PCs
distributed over multiple-LANs or the Internet to compute a large variety of resource …

摘要云计算环境下面向流程的数据密集型应用已被广泛应用于多个领域. 面对多数据中心的云
计算环境, 这类应用在数据布局方面遇到了新的挑战, 主要表现在如何减少跨数据中心的数据 …

Cloud computing has emerged as a promising computational infrastructure for cost-efficient
workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While …

Cloud computing systems provide high-performance computing resources and distributed
storage space to deal with data-intensive computations. Data scheduling between data …