A model, analysis, and protocol framework for soft state-based communication

"Soft state" is an often cited yet vague concept in network protocol design in which two or more network entities intercommunicate in a loosely coupled, often anonymous fashion. Researchers often define this concept operationally (if at all) rather than analytically: a source of soft state transmits periodic "refresh messages" over a (lossy) communication channel to one or more receivers that maintain a copy of that state, which in turn "expires" if the periodic updates cease. Though a number of crucial Internet protocol building blocks are rooted in soft state-based designs --- e.g., RSVP refresh messages, PIM membership updates, various routing protocol updates, RTCP control messages, directory services like SAP, and so forth --- controversy is building as to whether the performance overhead of soft state refresh messages justify their qualitative benefit of enhanced system "robustness". We believe that this controversy has risen not from fundamental performance tradeoffs but rather from our lack of a comprehensive understanding of soft state. To better understand these tradeoffs, we propose herein a formal model for soft state communication based on a probabilistic delivery model with relaxed reliability. Using this model, we conduct queueing analysis and simulation to characterize the data consistency and performance tradeoffs under a range of workloads and network loss rates. We then extend our model with feedback and show, through simulation, that adding feedback dramatically improves data consistency (by up to 55%) without increasing network resource consumption. Our model not only provides a foundation for understanding soft state, but also induces a new fundamental transport protocol based on probabilistic delivery. Toward this end, we sketch our design of the "Soft State Transport Protocol" (SSTP), which enjoys the robustness of soft state while retaining the performance benefit of hard state protocols like TCP through its judicious use of feedback.