  Components connected over a network influence each other and interact in
various ways. Examples of such systems are networks of computing nodes, which
the nodes interact by exchanging workload, for instance, for load balancing
purposes. In this paper, we first study the Influence Model, a networked Markov
chain framework, for modeling network interactions and discuss two key
limitations of this model, which cause it to fall short in modeling
constraint-based and dynamic interactions in networks. Next, we propose the
Dynamic and Constraint-based Influence Model (DCIM) to alleviate the
limitations. The DCIM extends the application of the Influence Model to more
general network interaction scenarios. In this paper, the proposed DCIM is
successfully applied to stochastic modeling of load balancing in networks of
computing nodes allowing for prediction of the load distribution in the system,
which is a novel application for the Influence Model. The DCIM is further used
to identify the optimum workload distribution policy for load balancing in
networked computing systems.
