18,198 research outputs found
Transferable knowledge for Low-cost Decision Making in Cloud Environments
Get PDFUsers of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each
provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which
typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud
applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly
large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is
not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL)
approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is
transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.
In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public
cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of
60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario
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