Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

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 Uploading all IoT Big Data to a centralized cloud for data analytics is infeasible
because of the excessive latency and bandwidth limitation of the Internet. A promising …

MapReduce has become a major computing model for data intensive applications. Hadoop,
an open source implementation of MapReduce, has been adopted by an increasingly …

The continuous advancement of cloud computing technology has driven the vigorous
development of cloud data centers. This manifests itself not only in increasing numbers, but …

Data heterogeneity and irregularity are key characteristics of big data applications that often
overwhelm the existing software and hardware infrastructures. In such context, the exibility …

Big Data processing systems (eg, Spark) have a number of resource configuration
parameters, such as memory size, CPU allocation, and the number of running nodes …

Traffic lights play an important role nowadays for solving complex and serious urban traffic
problems. How to optimize the schedule of hundreds of traffic lights has become a …

Configuration parameter optimization is an important means of improving the performance of
the MapReduce model. The existing parameter tuning methods usually optimize all …

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 …