An adaptive framework for tunable consistency and timeliness using replication

One of the well-known challenges in using replication to service multiple clients concurrently is that of delivering a timely and consistent response to the clients. In this paper, we address this problem in the context of client applications that have specific temporal and consistency requirements. These applications can tolerate a certain degree of relaxed consistency, in exchange for better response time. We propose a flexible QoS model that allows these clients to specify their temporal and consistency constraints. In order to select replicas to serve these clients, we need to control the inconsistency of the replicas, so that we have a large enough pool of replicas with the appropriate state to meet a client's timeliness, consistency, and dependability requirements. We describe an adaptive framework that uses lazy update propagation to control the replica inconsistency and employs a probabilistic approach to select replicas dynamically to service a client, based on its QoS specification. The probabilistic approach predicts the ability of a replica to meet a client's QoS specification by using the performance history collected by monitoring the replicas at runtime. We conclude with experimental results based on our implementation.