Exploring Design Alternatives for RAMP Transactions Through Statistical Model Checking

In this paper we explore and extend the design space of the recent RAMP (Read Atomic Multi-Partition) transaction system for large-scale partitioned data stores. Arriving at a mature distributed system design through implementation and experimental validation is a labor-intensive task, so that only a limited number of design alternatives can be explored in practice. The developers of RAMP did implement and validate three design alternatives for RAMP, and sketched three additional designs. This work addresses two questions: (1) How can the design space of a distributed transaction system such as RAMP be explored with modest effort, so that substantial knowledge about design alternatives can be gained before designs are implemented? and (2) How realistic and informative are the results of such design explorations? We answer the first question by: (i) formally modeling eight RAMP-like designs (five by the RAMP developers and three of our own) in Maude as probabilistic rewrite theories, and (ii) using statistical model checking of those models to analyze key performance metrics such as throughput, average latency, and degrees of strong consistency and read atomicity. We answer the second question by showing that our quantitative analyses: (i) are consistent with the experimental results obtained by the RAMP developers for their implemented designs; (ii) confirm the conjectures made by the RAMP developers for their other three unimplemented designs; and (iii) uncover some promising new designs that seem attractive for some applications.