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@InProceedings{2020:ICDS:Lynceus,
AUTHOR = {Casimiro, Maria and Didona, Diego and Romano, Paolo and Rodrigues, Luis and Zwaenepoel, Willy and Garlan, David},
TITLE = {Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs},
YEAR = {2020},
MONTH = {8-10 July},
BOOKTITLE = {The 40th International Conference on Distributed Computing Systems},
ADDRESS = {Singapore},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/ICDCS_2020_paper_2972020_ICDS_Lynceus.pdf},
ABSTRACT = {Modern data analytic and machine learning jobs
find in the cloud a natural deployment platform to satisfy their
notoriously large resource requirements. Yet, to achieve cost
efficiency, it is crucial to identify a deployment configuration that
satisfies user-defined QoS constraints (e.g., on execution time),
while avoiding unnecessary over-provisioning.
This paper introduces Lynceus, a new approach for the
optimization of cloud-based data analytic jobs that improves over
state-of-the-art approaches by enabling significant cost savings
both in terms of the final recommended configuration and of the
optimization process used to recommend configurations.
Unlike existing solutions, Lynceus optimizes in a joint fashion
both the cloud-related (i.e., which and how many machines to
provision) and the application-level (e.g. the hyper-parameters
of a machine learning algorithm) parameters. This allows for a
reduction of the cost of recommended configurations by up to
3.7X at the 90-th percentile with respect to existing approaches,
which treat the optimization of cloud-related and application level
parameters as two independent problems.
Further, Lynceus reduces the cost of the optimization process
(i.e., the cloud cost incurred for testing configurations) by
up to 11X. Such an improvement is achieved thanks to two
mechanisms: i) a timeout approach which allows to abort the
exploration of configurations that are deemed suboptimal, while
still extracting useful information to guide future explorations
and to improve its predictive model — differently from recent
works, which either incur the full cost for testing suboptimal
configurations or are unable to extract any knowledge from
aborted runs; ii) a long-sighted and budget-aware technique
that determines which configurations to test by predicting the
long-term impact of each exploration — unlike state-of-the-art
approaches for the optimization of cloud jobs, which adopt greedy
optimization methods.},
KEYWORDS = {Big data, Machine Learning, Resource Allocation}
}
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