Abstract Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both
research and industrial communities that allow expressing and processing distributed …

Enterprise users at different geographic locations generate large-volume data that is stored
at different geographic datacenters. These users may also perform big data analytics on the …

Different data transmission times, processing times which are difficult to predict and node-
dependent access times make MapReduce task scheduling rather complex. In this article …

We have witnessed a surge in both the big data applications being hosted by an assortment
of cloud vendors, and in the astronomical amount of data they produce and consume on a …

We are in the era of big data and cloud computing, large quantity of computing resource is
desperately needed to detect invaluable information hidden in the coarse big data through …

In the past twenty years, we have witnessed an unprecedented production of data worldwide
that has generated a growing demand for computing resources and has stimulated the …

The scale of big data has shown an explosive growth, which makes the processing of big
data put forward higher requirements on data centers, and a single data center can no …

As the explosive growth of the data volume, data center is playing a critical role to store and
process huge amount of data. Traditional single data center can no longer to adapt into …

We have witnessed a surge in big data applications being hosted by assorted cloud
vendors, and the astronomical amount of data they produce and consume on a daily basis …

Advances in the communication technologies, along with the birth of new communication
paradigms leveraging on the power of the social, has fostered the production of huge …