Dynamic workload peak detection for slack management

In this paper an analytical study on dynamism and possibilities on slack exploitation by dynamic power management is presented. We introduce a specific workload decomposition method for work required for (streaming) application processing data tokens (e.g. video frames) with work behaviour patterns as a mix of periodic and aperiodic patterns. It offers efficient and computationally light method for speculation on considerable work variations and its exploitation in energy saving techniques. It is used by a dynamic power management policy which has low overhead and reduces both requirements for buffering space, and deadline misses (increase QoS). We evaluate our policy in experiments on MPEG4 decoding of several different input sequences and present results.