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This paper presents the performance evaluation of a coherent distributed dynamic load balancing algorithm called the DGP (Decentralised Global Plans) algorithm. The performance evaluation is underpinned by a research methodology integrating a simulation-based performance evaluation with a prototype-based performance evaluation. The DGP algorithm addresses the issue of coherence and coordination in distributed dynamic load balancing algorithms. The DGP algorithm is based on a strategy called Global Plans (GP). This new approach first explicitly specifies a global goal and then design a strategy around this global goal such that each scheduler (1) takes into account local decisions made by other schedulers; (2) takes into account the effect of its local decisions on the overall system and (3) ensures load balancing. A simulation-based performance evaluation of DGP with two other well-known dynamic load balancing algorithms published in the literature shows that DGP performs consistently ...

In this study we modify an earlier approach developed for reducing the bias of the estimator for the mean response in simulation caused by the initial conditions. We try to balance the bias of the estimator in a simulation run by imposing a bias in the opposite direction in a companion run by suitably setting its initial conditions. We present analytical results for the bias of our estimator for AR(1) and MMs processes. We suggest making independent replications of the pairs of runs to construct a confidence interval for the mean response. We present some empirical results about the coverages and precisions of the confidence intervals. The results suggest that the idea of balancing a bias with a bias in the opposite direction is promising. 1

Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a university-based education in modeling and simulation to meet the challenges. We examine the factors affecting the composition of a modeling and simulation course. Based on the anticipated consequences, we propose potential solutions.