Today, many organizations need to operate on data
that is distributed around the globe. This is inevitable due to the
nature of data that is generated in different locations such as video
feeds from distributed cameras, log files from distributed servers,
and many others. Although centralized cloud platforms have
been widely used for data-intensive applications, such systems
are not suitable for processing geo-distributed data due to high
data transfer overheads. An alternative approach is to use an
Edge Cloud which reduces the network cost of transferring
data by distributing its computations globally. While the Edge
Cloud is attractive for geo-distributed data-intensive applications,
extending existing cluster computing frameworks to a wide-area
environment must account for locality. We propose Awan : a
new locality-aware resource manager for geo-distributed data-
intensive applications. Awan allows resource sharing between
multiple computing frameworks while enabling high locality
scheduling within each framework. Our experiments with the
Nebula Edge Cloud on PlanetLab show that Awan achieves up to
a 28% increase in locality scheduling which reduces the average
job turnaround time by approximately 18% compared to existing
cluster management mechanisms.
Jonathan, Albert; Chandra, Abhishek; Weissman, Jon.
Awan: Locality-aware Resource Manager for Geo-distributed Data-intensive Applications.
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