Distributed Management of CPU Resources for Time-Sensitive Applications

The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resources for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that fair resource allocation can be achieved through the proposed adjustment process when the CPU is overloaded. Experiments using the TrueTime Matlab toolbox show the validity of the proposed approach.