Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

The emerging class of wide-area streaming analytics faces the challenge of scarce and
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …

Low latency analytics on geographically distributed datasets (across datacenters, edge
clusters) is an upcoming and increasingly important challenge. The dominant approach of …

The Internet of Things (IoT) is the result of the convergence of sensing, computing, and
networking technologies, allowing devices of varying sizes and computational capabilities …

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of
application engines that are building upon the YARN resource management layer. The open …

To revamp with new creative age characterized by ongoing digital transformation, more and
more industries are capitalizing on digital innovation for their sustainable business growth …

Smartphones, wearables, and Internet-of-Things (IoT) devices produce a wealth of data that
cannot be accumulated in a centralized repository for learning supervised models due to …

Global-scale organizations produce large volumes of data across geographically distributed
data centers. Querying and analyzing such data as a whole introduces new research issues …

Dramatic changes in the technology landscape marked by increasing scales and
pervasiveness of compute and data have resulted in the proliferation of edge applications …

With growing data volumes generated and stored across geo-distributed datacenters, it is
becoming increasingly inefficient to aggregate all data required for computation at a single …