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Components connected over a network influence each other and interact invarious ways. Examples of such systems are networks of computing nodes, whichthe nodes interact by exchanging workload, for instance, for load balancingpurposes. In this paper, we first study the Influence Model, a networked Markovchain framework, for modeling network interactions and discuss two keylimitations of this model, which cause it to fall short in modelingconstraint-based and dynamic interactions in networks. Next, we propose theDynamic and Constraint-based Influence Model (DCIM) to alleviate thelimitations. The DCIM extends the application of the Influence Model to moregeneral network interaction scenarios. In this paper, the proposed DCIM issuccessfully applied to stochastic modeling of load balancing in networks ofcomputing nodes allowing for prediction of the load distribution in the system,which is a novel application for the Influence Model. The DCIM is further usedto identify the optimum workload distribution policy for load balancing innetworked computing systems.

Dynamic Constraint-based Influence Framework and its Application in Stochastic Modeling of Load Balancing