Extremal dependence analysis assesses the tendency of large values of components of a random vector to occur simultaneously. This kind of dependence information can be qualitatively different than what is given by correlation which averages over the total body of the joint distribution. Also, correlation may be completely inappropriate for heavy tailed data.

We review some techniques, somewhat exploratory in nature, for assessing asymptotic independence for internet file size, throughput and duration of transfer. In an attempt to formalize a procedure, we study a summary statistic called the extremal dependence measure (EDM), a measure of the tendency of large values of components of a random vector to occur simultaneously and show consistency and asymptotic normality properties for the standard case of multivariate regular variation.