Big Data (BD) platforms have a long tradition of leveraging trends and technologies from the
broader computer network and communication community. For several years, dedicated …

Big data systems are sufficiently stable to store and process a massive volume of rapidly
changing data. However, big data systems are composed of large-scale hardware resources …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Recent trends in big data have shown that the amount of data continues to increase at an
exponential rate. This trend has inspired many researchers over the past few years to …

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 …

MapReduce (MR) is a criterion of Big Data processing model with parallel and distributed
large datasets. This model knows difficult problems related to low-level and batch nature of …

In many MapReduce applications, there is an unbalanced distribution of intermediate map-
outputs to the reducers. The partitioner determines the load on the reducers. The completion …

Hadoop MapReduce reactively detects and recovers faults after they occur based on the
static heartbeat detection and the re-execution from scratch techniques. However, these …

Processing huge and complex data to obtain useful information is challenging, even though
several big data processing frameworks have been proposed and further enhanced. One of …

Internet of Things, edge computing devices, the widespread use of artificial intelligence and
machine learning applications, and the extensive adoption of cloud computing pose …