Nowadays, there is an accelerating need to efficiently and timely handle large amounts of
data that arrives continuously. Streams of big data led to the emergence of several …

Abstract Existing Data Stream Processing (DSP) systems perform poorly while encountering
heavy workloads, particularly on clustered set of (heterogeneous) computers. Elasticity and …

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 …

In this study, we investigated the problem of scheduling streaming applications on a
heterogeneous cluster environment and, based on our previous work, developed the …

In a big data stream computing environment, the arrival rate of data streams usually
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of
data that arrives continuously. Streams of big data led to the emergence of Distributed …

The recent advancements in storage technologies have popularized the use of tiered
storage systems in data-intensive compute clusters. The Hadoop Distributed File System …

Job scheduling is a fundamental problem in cloud data center, which plays an essential role
in the makespan, resource utilization and maintenance of scheduling security, it has …

Reducing latency has become the focus of task scheduling research in distributed big data
stream computing systems. Currently, most task schedulers in big data stream computing …

Big data applications have become an integral part of many intelligent systems, enabling
better business decision making, by extracting useful information from historical data. This …