Abstract
Nowadays, data volumes increase exceptionally, a lot of information comes from different sources, for example, from mobile phones, sensors, traffic, etc. All information from these sources can be represented as a data streams, which can grow up and fall in time in their size. In the first case, data processing requires optimization via dynamic resource allocation in order to decrease processing time, in the second case, it requires optimization related with resources deallocation because removing unnecessary resources can decrease the total cost. The question is how to identify optimal amount of resources to satisfy required processing delay under certain volume of workload? Current implementation of Apache Spark Streaming and existing models can’t give us such possibility. In this paper, we propose adaptive performance model, which can dynamically scale up and down Apache Spark Streaming platform on the AWS.
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