A probabilistic model for the performance analysis of a distributed task allocation algorithm

In this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. In this case, we move a step forward and calculate the variance of the global cost. This second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. The normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. This approximation enables us to compare our algorithm theoretically in different cases.