We review the literature on algorithmic management (AM) to bridge the gap between this
emerging research area and the well-established theory and research on work design. First …

Abstract Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both
research and industrial communities that allow expressing and processing distributed …

Datacenter-scale clusters are evolving toward heterogeneous hardware architectures due to
continuous server replacement. Meanwhile, datacenters are commonly shared by many …

While Hadoop ecosystems become increasingly important for practitioners of large-scale
data analysis, they also incur tremendous energy cost. This trend is driving up the need for …

Streaming data analytics has become increasingly vital in many applications such as
dynamic content delivery (eg, advertisements), Twitter sentiment analysis, and security event …

Big data frameworks such as Apache Spark is becoming prominent to perform large-scale
data analytics jobs in various domains. However, due to limited resource availability, the …

At present, big data is very popular, because it has proved to be much successful in many
fields such as social media, E-commerce transactions, etc. Big data describes the tools and …

Most programs are repetitive, where similar behavior can be seen at different execution
times. Proposed on-line systems automatically group these similar intervals of execution into …

In this paper, we make a case for providing job completion time estimates to GPU cluster
users, similar to providing the delivery date of a package or arrival time of a booked ride. Our …

While MapReduce is inherently designed for batch and high throughput processing
workloads, there is an increasing demand for non-batch processes on big data, eg …