Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Reliable and Rapid Elasticity for Streaming Dataflows on Clouds   The pervasive availability of streaming data is driving interest in
distributed Fast Data platforms for streaming applications. Such
latency-sensitive applications need to respond to dynamism in the input rates
and task behavior using scale-in and -out on elastic Cloud resources. Platforms
like Apache Storm do not provide robust capabilities for responding to such
dynamism and for rapid task migration across VMs. We propose several dataflow
checkpoint and migration approaches that allow a running streaming dataflow to
migrate, without any loss of in-flight messages or their internal tasks states,
while reducing the time to recover and stabilize. We implement and evaluate
these migration strategies on Apache Storm using micro and application
dataflows for scaling in and out on up to 2-21 Azure VMs. Our results show that
we can migrate dataflows of large sizes within 50 sec, in comparison to Storm's
default approach that takes over $100~sec$. We also find that our approaches
stabilize the application much earlier and there is no failure and
re-processing of messages.

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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg