Energy driven adaptivity in stream parallel computations

Determining the right amount of resources needed for a given computation is a critical problem. In many cases, computing systems are configured to use an amount of resources to manage high load peaks even though this cause energy waste when the resources are not fully utilised. To avoid this problem, adaptive approaches are used to dynamically increase/decrease computational resources depending on the real needs. A different approach based on Dynamic Voltage and Frequency Scaling (DVFS) is emerging as a possible alternative solution to reduce energy consumption of idle CPUs by lowering their frequencies. In this work, we propose to tackle the problem in stream parallel computations by using both the classic adaptivity concepts and the possibility provided by modern CPUs to dynamically change their frequency. We validate our approach showing a real network application that performs Deep Packet Inspection over network traffic. We are able to manage bandwidth changing over time, guaranteeing minimal packet loss during reconfiguration and minimal energy consumption.