Efficient Process Mapping in Geo-Distributed Cloud Data
Centers
Event Type
Paper
TimeTuesday, November 14th3:30pm -
4pm
Location405-406-407
DescriptionRecently, various applications including data analytics
and machine learning have been developed for
geo-distributed cloud data centers. For those
applications, the ways to map parallel processes to
physical nodes (i.e., “process mapping”) could
significantly impact the performance of the applications
because of non-uniform communication cost in such
geo-distributed environments. While process mapping has
been widely studied in grid/cluster environments, few of
the existing studies have considered the problem in
geo-distributed cloud environments. In this paper, we
propose a novel model to formulate the geo-distributed
process mapping problem and develop a new method to
efficiently find the near optimal solution. Our
algorithm considers both the network communication
performance of geo-distributed data centers as well as
the communication matrix of the target application.
Evaluation results with real experiments on Amazon EC2
and simulations demonstrate that our proposal achieves
significant performance improvement (50% on average)
compared to the state-of-the-art algorithms.
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