Optimizing the performance of dynamic load balancing toolkits and applications requires the adjustment of several runtime parameters; however, determining sufficiently good values for these parameters through repeated experimentation can be an expensive and prohibitive process. We describe an analytic modeling method which allows developers to study and optimize adaptive application performance in the presence of dynamic load balancing. To aid tractibility, we first derive a "bi-modal" step function which simplifies and approximates task execution behavior. This allows for the creation of an analytic modeling function which captures the dynamic behavior of adaptive and asynchronous applications, enabling accurate predictions of runtime performance. We validate our technique using synthetic microbenchmarks and a parallel mesh generation application and demonstrate that this technique, when used in conjunction with the PREMA runtime toolkit, can offer users significant performance improvements over several well-known load balancing tools used in practice today.