Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

We consider a scenario involving computations over a massive dataset stored distributedly
across multiple workers, which is at the core of distributed learning algorithms. We propose …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper
we design novel gradient codes using tools from classical coding theory, namely, cyclic …

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 …

Slow running or straggler tasks can significantly reduce computation speed in distributed
computation. Recently, coding-theory-inspired approaches have been applied to mitigate …

We can increase the efficiency of public cloud datacenters by harvesting allocated but
temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics …

Recently, with the expansion of communications and generated data, the need for
processing this high volume of data in minimum time and maximum speed has increased …

Hadoop is the most economical and cheap software framework that allows distributed
storage and parallel processing of more extensive data sets. Hadoop distributed file system …

Performance of distributed optimization and learning systems is bottlenecked by" straggler"
nodes and slow communication links, which significantly delay computation. We propose a …