Flexible and Efficient Decision-Making for Proactive Latency-Aware Self-Adaptation
Venue: T ransactions on Autonomous and Adaptive Systems (TAAS) Authors: Gabriel A. Moreno, Javier Camara, David Garlan, Bradley Schmerl The title essentially encapsulates the problem which this paper is addressing. The setting is a Markov Decision Process, but with deterministic adaptations that do not effect the evolution of the environment. Restated another way, decisions do not impact the state, but rather the reward (and penalty). This leads to a separate notion of environment state and system state . Note that the utility (reward) may be a factor of both. The innovation of this paper lies specifically in encapsulating that while deterministic, the different actions have different delays in terms of system state. The environment evolution is modeled via a discrete time markov chain (DTMC), which can be used to generate a partial probability tree. Specifically, an Extended Pearson-Tukey (EP-T) three point approximation. To encode latency, the progress of an adaptation is e...