Deriving Parameters for Open and Closed QN Models of Operational Systems Through Black Box Optimization
Mahmoud Awad, Daniel A. Menascé · 2017
Black-box modeling techniques are used when modeling computer systems with unknown internal structure or behavior and/or when it is not feasible or too time consuming to monitor a running computer system. The main challenge in these situations lies in estimating values for the parameters of these models, especially the values of service demands at the various devices for each transaction class. These estimates have to be compliant with the input-output relationships observed through measurements. This means that solving a model of the system with the estimated parameters should yield the same outputs (e.g., response times) for the same inputs (e.g., arrival rates or concurrency level). This paper presents a method for automatically estimating service demands for open, closed, single and multiclass queuing networks (QN). The method is based on casting the estimation problem as a non-linear optimization problem. However, because the solution of closed QNs does not have a closed form, we need to resort to black-box optimization techniques. The parameter estimation method presented here is part of iModel, a framework for automatically deriving performance models of systems whose detailed characteristics (structure and behavior) are unknown. Other portions of the framework were discussed in detail in previous publications by the authors. This paper illustrates the ideas through several numerical examples and then applies them to a multi-tiered operational system running OFBiz. The estimated service demands closely satisfy the input-output relationships at various workload intensity levels and can be used for prediction purposes.