Nowadays energy consumption problem is a major issue for data centers. The energy consumption increases significantly along with its CPU frequency getting higher. With Dynamic Voltage and Frequency Scaling (DVFS) techniques, CPU could be set to a suitable working frequency during the running time according to the workload. On the other side, reducing frequency implies that more servers will be ...

Data locality is a fundamental issue for data-parallel applications. Considering MapReduce in Hadoop, the map task scheduling part requires an efficient algorithm which takes data locality into consideration; otherwise, the system may become unstable under loads inside the system’s capacity region and jobs may experience longer completion times which are not of interest. The data chunk needed f...

Data centers store a massive amount of data in a large number of servers built by commodity hardware. To maintain data integrity against server failures, erasure codes have been extensively deployed in modern data centers to provide a higher level of failure tolerance with less storage overhead than replication. Yet, compared to replication, disseminating erasure-coded data from a source server...

Unlike traditional high performance computing environment, such as supercomputers, the cloud computing is a collection of interconnected and virtualized computing resources that are managed to be one or more unified computing resources. The Cloud environment constitutes a heterogeneous and a highly dynamic environment. Failures on the data centers storage nodes are normal rather than exceptiona...

As the amount of data explodes rapidly, more and more organizations tend to use data centers to make effective decisions and gain a competitive edge. Big data applications have gradually dominated the data centers’ workloads, and hence it has been increasingly important to understand their behaviour in order to further improve the performance of data centers. Due to the constantly increased gap...

Distributed computing applications like MapReduce transfer massive amount of data between their successive processing stages. These data transfers, such as common shuffle and incast communication patterns, contribute most of the network traffic and thus have severe impacts on application performances. Despite such impacts, there has been relatively little work on decreasing the amount of traffi...

We present a data mining approach to model the cooling infrastructure in data centers, particularly the chiller ensemble. These infrastructures are poorly understood due to the lack of “first principles” models of chiller systems. At the same time, they abound in data due to instrumentation by modern sensor networks. We present a multi-level framework to transduce sensor streams into an actiona...