Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses
Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process …

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

Abstract Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields.
However, there are very few cloud resource provisioning and scheduling algorithms tailored …

Cloud computing, a novel and promising model of Service-oriented computing, provides a
pay-per-use framework to solve large-scale scientific and business workflow applications …

Scientific workflow applications are collections of several structured activities and fine-
grained computational tasks. Scientific workflow scheduling in cloud computing is a …

There are many high-dimensional multiview data in various big data applications. It is very
difficult to deal with those high-dimensional multiview data for the classic clustering …

The use of storage tiering is becoming popular in data-intensive compute clusters due to the
recent advancements in storage technologies. The Hadoop Distributed File System, for …

MapReduce is a common framework that effectively processes multi-petabyte data in a
distributed manner. Therefore, MapReduce is widely used in heterogeneous environments …

In the current decade, doing the search on massive data to find “hidden” and valuable
information within it is growing. This search can result in heavy processing on considerable …

With the development of content-sharing and collaborative computing services such as
online social networks, scientific workflow, there are huge amounts of data generated. To …