Over the last decade, several advancements have happened in distributed and parallel
computing. A lot of data is generated daily from various sources, and this speedy data …

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 …

Processing large volumes of data has presented a challenging issue, particularly in data-
redundant systems. As one of the most recognized models, the conditional random fields …

MapReduce is by far one of the most successful realizations of large-scale data-intensive
cloud computing platforms. When to start the reduce tasks is one of the key problems to …

A MapReduce scheduling algorithm plays a critical role in managing large clusters of
hardware nodes and meeting multiple quality requirements by controlling the order and …

Nowadays, energy costs are the most important factor in cloud computing. Therefore, the
implementation of energy-aware task scheduling methods is of utmost importance. A task …

The original task scheduling algorithm of Hadoop cannot meet the performance
requirements of heterogeneous clusters. According to the dynamic change of load of each …

As the game industry matures, processing complex game logics in a timely manner is no
longer an insurmountable problem. However, current cloud-based mobile gaming solutions …

Stochastic number generator (SNG) is one important component of stochastic computing
(SC). An SNG usually consists of a random number source (RNS) and a probability …

In recent years, big data systems have become an active area of research and development.
Stream processing is one of the potential application scenarios of big data systems where …