A framework for incremental deployment strategies for router-assisted services

Incremental deployment of a new network service or protocol is typically a hard problem, especially when it has to be deployed in the routers. First, an incrementally deployable protocol is needed. Second, a study of the performance impact of incremental deployment should be carried out to evaluate deployment strategies. Choosing the wrong strategy can be disastrous, as it may inhibit reaping the benefits of an otherwise robust service, and prevent widespread adoption. Unfortunately, to date there has been no systematic evaluation of incremental deployment for such services. Our research work is focused on the second aspect, namely the performance impact of incremental deployment of router-assisted services. We take the first step to define a framework for evaluating incrementally deployable services, which consists of three parts: (a) selection and classification of deployment strategies; (b) definition of performance metrics; and (c) systematic evaluation of deployment strategies. As a case study for our framework, we evaluate the performance of router-assisted reliable multicast protocols. Although our framework is still evolving, our results clearly demonstrate that the choice of a strategy has a substantial impact on performance, and thus affirms the need for systematic evaluation of incremental deployment. Our case study includes two router-assisted reliable multicast protocols, namely PGM and LMS. We make several interesting observations: (a) the performance of different deployment strategies varies widely; for example, with some strategies, both PGM and LMS approach full deployment performance with as little as 5% of the routers deployed, but with other strategies up to 80% deployment may be needed to approach the same level; (b) our sensitivity analysis reveals relatively small variation in the results in most cases; and (c) the penalty associated with partial deployment is different for each of these protocols; PGM tends to impact the network, whereas LMS impacts the endpoints.