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 …

The continuous increase in computational capacity over the past years has produced an
overwhelming flow of data or big data, which exceeds the capabilities of conventional …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

MapReduce is emerging as an important programming model for large-scale data-parallel
applications such as web indexing, data mining, and scientific simulation. Hadoop is an …

Modern big data applications tend to prefer a cluster computing approach as they are linked
to the distributed computing framework that serves users jobs as per demand. It performs …

Data center applications present significant opportunities for multiplexing server resources.
Virtualization technology makes it easy to move running application across physical …

A heterogeneous cloud system, for example, a Hadoop 2.6. 0 platform, provides distributed
but cohesive services with rich features on large‐scale management, reliability, and error …

MapReduce is an effective tool for parallel data processing. One significant issue in practical
MapReduce applications is data skew: the imbalance in the amount of data assigned to …

Increased complexity and scale of virtualized distributed systems has resulted in the
manifestation of emergent phenomena substantially affecting overall system performance …

Many resource management techniques for task scheduling, energy and carbon efficiency,
and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime …