Cloud environments have provided great help for the development of big data. Today, cloud
environments have become the most active platform for big data analysis applications …

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

Large data centers are currently the mainstream infrastructures for big data processing. As
one of the most fundamental tasks in these environments, the efficient execution of …

By combing edge computing and parallel computing, distributed edge computing has
emerged as a new paradigm to accelerate computation at the edge. Considering the …

Scheduling plays an important role in improving the performance of big data-parallel
processing. Spark is an in-memory parallel computing framework that uses a multi-threaded …

By integrating edge computing with parallel computing, distributed edge computing (DEC)
makes use of distributed devices in edge networks to perform computing in parallel, which …

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual
Machines (VMs). As these applications are communication-intensive, optimizing network …

Enhanced wireless communication improves the connectivity of vehicular networks in which
vehicles are utilized as infrastructures for communication and computation. Thus, a new …

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 …

Spark is widely used for its fast in-memory processing. It is important to improve energy
efficiency under deadline constrains. In this paper, a Task Performance Clustering of Best …