Real-time adaptable resource allocation for distributed data-intensive applications over cloud and edge environments



Applications performance is strongly linked with the total load, the application deployment architecture and the amount of resources allocated by the cloud or edge computing environments. Considering that the majority of the applications tends to be data intensive, the load becomes quite dynamic and depends on the data aspects, such as the data sources locations, their distribution and the data processing aspects within an application that consists of micro-services. In this paper we introduce an analysis and prediction model that takes into account the characteristics of an application in terms of data aspects and the edge computing resources attributes, such as utilization and concurrency, in order to propose optimized resources allocation during runtime.

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