Cloud computing, rapidly emerging as a new computation paradigm, provides agile and
scalable resource access in a utility-like fashion, especially for the processing of big data. An …

Hadoop and Spark are widely used distributed processing frameworks for large-scale data
processing in an efficient and fault-tolerant manner on private or public clouds. These big …

In the era of global-scale services, organisations produce huge volumes of data, often
distributed across multiple data centres, separated by vast geographical distances. While …

Previous work has shown that benchmark and application performance in public cloud
computing environments can be highly variable. Utilizing Amazon EC2 traces that include …

Many geo-distributed data analytics (GDA) systems have focused on the network
performance-bottleneck: inter-data center network bandwidth to improve performance …

MapReduce has proven remarkably effective for a wide variety of data-intensive
applications, but it was designed to run on large single-site homogeneous clusters …

With the globalization of service, organizations continuously produce large volumes of data
that need to be analysed over geo-dispersed locations. Traditionally central approach that …

Purpose-built clusters permeate many of today's organizations, providing both large-scale
data storage and computing. Within local clusters, competition for resources complicates …

Today, data-intensive applications rely on geographically distributed systems to leverage
data collection, storing and processing. Data locality has been seen as a prominent …

The MapReduce programming model offers a simple and efficient way of performing
distributed computation over large data sets. To enable the usage of MapReduce in the …