Most global enterprises and application service providers need to use resources from multiple clouds managed by different cloud service providers, located throughout the world. The ability to manage these geographically distributed resources requires use of specialized management and control platforms. Such platforms allow enterprises to deploy and manage their applications across remote clouds that meet their objectives. Generally, these platforms are multi-threaded, distributed and highly complex. They need to be optimized to perform well and be cost effective for all players. For optimization to succeed, it has to be preceded by profiling and performance evaluation. In this paper we present techniques to profile such platforms using OpenADN as a running example. The effectiveness of using profiling data with the two factor full factorial design to analyze the effect of workloads and other important factors on the performance, has been demonstrated. It is seen that the workload, of varying number of users and hosts, does not have a significant impact on the performance. On the other hand, functions like host creation and polling have significant impact on the execution time of the platform software, indicating potential gains from optimization.