Dynamic code transformation systems are steadily gaining acceptance in computing environments for services such as program optimization, translation, instrumentation and security. Code transformation systems are required to perform complex and time consuming tasks such as costly program analysis and apply transformations (i.e. instrumentation, translation etc.) As these steps are applied to all code regions (regardless of characteristics), the transformation overhead can be significant. Once transformed, the remaining overhead is determined by the performance of the translated code. Current code transformation systems can only become part of mainstream computing only if these overheads are eliminated. Nevertheless, certain application and computing environments exist in which code transformation systems can be effectively deployed. This thesis identifies two such environments, persistence and mixed execution. Persistence leverages previous execution characteristics to address the transformation overhead. This is accomplished by capturing the translated executions at the end of their first invocation. The captured executions are cached on disk for re-use. All subsequent invocations of the run-time system using the same application cause the system to reuse the cached executions. Since applications exhibit similar behavior across varying input data sets, this execution model successfully diminishes the transformation overhead across multiple invocations. Persistence in the domain of dynamic binary instrumentation is highlighted as an example. Mixed execution accepts that the performance of the code generated by today’s code transformation systems is in no position to compete with original execution times. Therefore, this technique proposes executing a mix of the original and translated code sequences to keep the translated code performance penalties within bounds. This execution model is a more effective alternative to pure Just-in-Time compiler-based code transformation systems, when low overheads and minimal architectural perturbation are the critical constraints required to be met. A dynamic compilation framework for controlling microprocessor energy and performance using this model is presented in light of its effectiveness and practicality.