Bounds-Based Performance Analysis For Distributed Systems With Variabilities And Uncertainties In Workload

Bounding techniques for queueing network models used to analyze the performance of parallel and distributed computer systems accept single values as model inputs. Uncertainties or variabilities in service demands may exist in many types of systems making the use of a single mean value for each model parameter inappropriate and causing the conventional modeling approach to become ineffective. Thus, the conventional bounding techniques are not directly applicable to systems with variabilities and uncertainties in workload. This paper proposes to use histograms for characterizing one or more model parameters that are associated with uncertainty and/or variability. The adaptation of the well-known asymptotic bounds as well as balanced job bounds to histogram parameters is presented in the paper. One or more input parameters for the model can be specified as a histogram. It is shown that not considering observed variabilities may lead to highly inaccurate and incorrect bounds on system performance. The proposed technique is applied to an illustrative queueing network model example modeling the client-server architecture of the computer system of a telemarketing company.