Probabilistic Sequential Consistency in Social Networks

Researchers have proposed numerous consistency models in distributed systems that offer higher performance than classical sequential consistency (SC). Even though these models do not guarantee sequential consistency; they either behave like an SC model under certain restrictive scenarios, or ensure SC behavior for a part of the system. We propose a different line of thinking where we try to accurately estimate the number of SC violations, and then try to adapt our system to optimally tradeoff performance, resource usage, and the number of SC violations. In this paper, we propose a generic theoretical model that can be used to analyze systems that are comprised of multiple sub-domains – each sequentially consistent. It is validated with real world measurements. Next, we use this model to propose a new form of consistency called social consistency, where socially connected users perceive an SC execution, whereas the rest of the users need not. We create a prototype social network application and implement it on the Cassandra key-value store. We show that our system has 2.4× more throughput than Cassandra and provides 37% better quality-of-experience.