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

Recent work has made the case for geo-distributed analytics, where data collected and
stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive …

Many large organizations collect massive volumes of data each day in a geographically
distributed fashion, at data centers around the globe. Despite their geographically diverse …

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 …

Running data-parallel jobs across geo-distributed sites has emerged as a promising
direction due to the growing need for geo-distributed cluster deployment. A key difference …

As cloud services grow to span more and more globally distributed datacenters, there is an
increasingly urgent need for automated mechanisms to place application data across these …

Typically called big data processing, processing large volumes of data from geographically
distributed regions with machine learning algorithms has emerged as an important …

Large data centers are currently the mainstream infrastructures for big data processing. As
one of the most fundamental tasks in these environments, the efficient execution of …

Big data analytics has attracted close attention from both industry and academic because of
its great benefits in cost reduction and better decision making. As the fast growth of various …

Typically called big data processing, analyzing large volumes of data from geographically
distributed regions with machine learning algorithms has emerged as an important …