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 …

Storm has emerged as a promising computation platform for stream data processing. In this
paper, we first show inefficiencies of the current practice of Storm scheduling and challenges …

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 …

MapReduce/Hadoop framework has been widely used to process large-scale datasets on
computing clusters. Scheduling map tasks with data locality consideration is crucial to the …

In this paper, we focus on general-purpose Distributed Stream Data Processing Systems
(DSDPSs), which deal with processing of unbounded streams of continuous data at scale …

The majority of large-scale data intensive applications executed by data centers are based
on MapReduce or its open-source implementation, Hadoop. Such applications are executed …

Complex query types, huge data volumes, and very high read/update ratios make the
indexing techniques designed and tuned for traditional database systems unsuitable for …

The explosive growth of demands on big data processing imposes a heavy burden on
computation, storage, and communication in data centers, which hence incurs considerable …

Systems and methods for resource aware scheduling of pro cesses in a distributed
computing environment are described herein. One aspect provides for accessing at least …

In a large cloud data center HPC system, a critical problem is how to allocate the submitted
tasks to heterogeneous servers that will achieve the goal of maximizing the system's gain …