Towards Systematic and Accurate Environment Selection for Emerging Cloud Applications
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As cloud computing is gaining popularity, many application owners are migrating their
applications into the cloud. However, because of the diversity of the cloud environments
and the complexity of the modern applications, it is very challenging to find out which
cloud environment is best fitted for one's application.
In this dissertation, we design and build systems to help application owners select the
most suitable cloud environments for their applications. The first part of this thesis focuses
on how to compare the general fitness of the cloud environments. We present CloudCmp,
a novel comparator of public cloud providers. CloudCmp measures the elastic computing,
persistent storage, and networking services offered by a cloud along metrics that directly
reflect their impact on the performance of customer applications. CloudCmp strives to
ensure fairness, representativeness, and compliance of these measurements while limiting
measurement cost. Applying CloudCmp to four cloud providers that together account
for most of the cloud customers today, we find that their offered services vary widely in
performance and costs, underscoring the need for thoughtful cloud environment selection.
From case studies on three representative cloud applications, we show that CloudCmp can
guide customers in selecting the best-performing provider for their applications.
The second part focuses on how to let customers compare cloud environments in the
context of their own applications. We describe CloudProphet, a novel system that can
accurately estimate an application's performance inside a candidate cloud environment
without the need of migration. CloudProphet generates highly portable shadow programs
to mimic the behavior of a real application, and deploys them inside the cloud to estimate
the application's performance. We use the trace-and-replay technique to automatically
generate high-fidelity shadows, and leverage the popular dispatcher-worker pattern
to accurately extract and enforce the inter-component dependencies. Our evaluation in
three popular cloud platforms shows that CloudProphet can help customers pick the bestperforming
cloud environment, and can also accurately estimate the performance of a
variety of applications.
Li, Ang (2012). Towards Systematic and Accurate Environment Selection for Emerging Cloud Applications. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/5814.
Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.