Improving an Artificial Hormone System’s Time Bounds Using Task Allocation Signals
Eric Hutter, Robin Lakos, Uwe Brinkschulte · 2022
This paper proposes an extension to the Artificial Hormone System (AHS) in order to improve its time bounds. The AHS is a middleware to distribute tasks to a distributed system’s nodes in a decentralized manner based on Organic Computing principles. The AHS’ task distribution has previously been extended by a priority-based approach that allows to prioritize tasks, allowing to control the order of their allocation during the system’s initial self-configuration or self-healing after a node failure. However, this extension worsens the hard time bounds guaranteed by the AHS compared to its original implementation without priority support. We thus propose so-called task allocation signals that improve these time bounds. This approach results in a speedup factor of up to (2 − 1/m) where m is the number of tasks to distribute in the system, restoring the original AHS’ time bounds while keeping the priority support intact. Evaluations conducted using a cycle-accurate AHS simulator fully confirm the theoretical results presented.