Data centers are growing exponentially (in number and size) to accommodate the escalating
user and application demands. Likewise, the concerns about the environmental impacts …

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 …

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 …

To reduce the impact of network congestion on big data jobs, cluster management
frameworks use various heuristics to schedule compute tasks and/or network flows. Most of …

Traditional HPC architectures separate compute nodes and storage nodes, which are
interconnected with high speed links to satisfy data access requirements in multi-user …

MapReduce clusters are usually multi-tenant (ie, shared among multiple users and jobs) for
improving cost and utilization. The performance of jobs in a multi-tenant MapReduce cluster …

Nowadays data-intensive applications for processing big data are being hosted in the cloud.
Since the cloud environment provides virtualized resources for computation, and data …

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 …

MapReduce emerges as an important distributed programming paradigm for large-scale
applications. Running MapReduce applications in clouds presents an attractive usage …

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 …