On optimization of continuous-time Markov networks in distributed computing

The paper presents a new stochastic model for studying the optimization of functioning rules in distributed computing. In this model a network is represented by a finite number of continuous-time homogeneous Markov processes which are connected by relations between entries of their intensity matrices. Good functioning rules are those optimizing a guide function defined according to the context. Two specific optimization problems are studied: a problem of resource allocation with conflicts between processes, and a problem of access to shared resources. The latter is a linearly constrained nonconvex problem with an objective function which is a sum of ratios of linear functions of special form.