Doctor of Philosophy / Department of Computing and Information Sciences / Gurdip Singh / To increase their applicability, distributed algorithms are typically written to work with any application on any network. This flexibility comes at the cost of performance since these 'general purpose' algorithms are written with the worst case scenario in mind. A distributed algorithm written for a specific application or a class of application is fine tuned to the properties of the application and can give a better performance when compared to the general purpose one. In this work, we propose two mechanisms in which we can optimize a general purpose algorithm - Alg based on the application - App using it. In the first approach, we analyze the specification of App to identify patterns of communication in its communication topology. These properties are then used to customize the behavior of the underlying distributed algorithm Alg. To demonstrate this approach, we study applications specified as component based systems where application components communicate via events and distributed algorithms to enforce ordering requirements on these events. We show how our approach can be used to optimize event ordering algorithms based on communication patterns in the applications. In the second approach, rather than analyzing the application specification, we assume that the developer provides application properties - I_App which are invariants for the optimization process. We assume that the algorithm is written and annotated in a format that is amenable to analysis. Our analysis algorithm then takes as input the application invariants and the annotated algorithm and looks for potential functions in the algorithm which are redundant in the context of the given application. In particular, we first look for function invocations in the algorithm whose post-conditions are already satisfied as a result of the application invariants. Each such invocation is considered as a potential redundant module. We further analyze the distributed algorithm to identify the impact of the removal of a specific invocation on the rest of the algorithm. We describe an implementation of this approach and demonstrate the applicability using a distributed termination detection algorithm.