Configuration capabilities are important for modern advanced network services. Network conditions and user populations have been significantly diversified after decades of evolution of the Internet. Configuration capabilities allow network services to be adapted to spatial, temporal, and managerial variations in application requirements and service operation conditions. Network service providers need to decide on the best configuration. Ideally, a network service should have all of its components optimally configured to most effectively deliver the functionality for which it was designed. The optimal configuration, however, is always a compromise between different metrics. To decide on an optimal configuration, the prominent performance and cost metrics must be identified, modeled, and quantified. Optimization objective functions and constraints that combine these metrics should be formulated and optimization techniques should be developed. More important, in the scenarios where the application requirements and system conditions change over time, the service configuration needs to be dynamically adjusted and strategies that guide the reconfiguration decisions need to be developed. Because the actual process of configuring a network service incurs configuration costs, an optimal reconfiguration strategy should be one that achieves a tradeoff between the (re)configuration costs and static optimization objectives. Furthermore, such tradeoffs must be based on the consideration of long-term benefits instead of short-term interest. This thesis focuses on understanding the strategies for dynamic (re)configuration of advanced network services positioned above the Transport Layer. Specifically, this thesis investigates the configuration and more important dynamic reconfiguration strategies for two types of advanced network services: Service Overlay Networks, and Content Resiliency Service Networks. Unlike those network services whose configuration involves mainly arrangement of hard-wired components, these network services have the ability to change service configuration in small time scales. This makes the modeling of application requirements and system condition dynamics not only possible but also meaningful and potentially useful. Our goal is to develop modeling and optimization techniques for network service configuration and dynamic reconfiguration policies. We also seek to understand how effective techniques can improve the performance or reduce the cost of these advanced network services, thus demonstrating the advantage of allowing configurability in these advanced network services.