Hierarchical approach to building generative networkload models

Performance evaluation study of computer networks requires a concise description of the workload under which the performance is to be evaluated. The performance evaluation of networks is an important field of study today, because of the increasing usage of computer networks. In the context of network sizing or tuning, it is often necessary to conduct the performance evaluation studies under different load conditions. The repeatability of the experiments for different workload profiles, requires that the workload models generate the workload profiles parametrically. Such a model, should preferably be time-invariant, consistent and generative. We propose a hierarchical approach for building generative networkload ( workload for networks) models, based on the Context Free Grammar (CFG). We view the networkload as a sequence that can be generated from the rules of a CFG. Our approach combines the established practice of viewing the workload as “consisting of a hierarchy” and the CFG description, to produce a generative networkload model. The time-invariance and generative nature are verified experimentally. The usefulness of the networkload model, in the study of a typical resource management problem of a network, such as the optimal allocation of clients to servers, is illustrated by using the generative model as input descriptor to a queuing network model of a single server network.