Tackling latency via replication in distributed systems

Consistently high reliability and low latency are twin requirements common to many forms of distributed processing; for example, server farms and mirrored storage access. To address them, we consider replication of requests with canceling – i.e. initiate multiple concurrent replicas of a request and use the first successful result returned, canceling all outstanding replicas. This scheme has been studied recently, but mostly for systems with a single central queue, while server farms exploit distributed resources for scalability and robustness. We develop an approximate stochastic model to determine the response time distribution in a system with distributed queues, and compare its performance against its centralized counterpart. Validation against simulation indicates that our model is accurate for not only the mean response time but also its quantiles, which are particularly relevant for deadline-driven applications. Further, we show that in the distributed setup, replication with canceling has the potential to reduce response times, even at relatively high utilization. We also find that it offers response times close to those of the centralized system, especially at medium-to-high request reliability. These findings support the use of replication with canceling as an effective mechanism for both fault- and delay-tolerance.