An Extremum Seeking Algorithm for Message Batching in Total Order Protocols

Message batching is a well-known optimization technique to maximize throughput of networked services. The manual configuration of the appropriate batching level is however a time consuming and not trivial task. Too low batching values can in fact render the system unstable in presence of high loads, excessively high batching values, on the other hand, can lead to high latency at low load, which may be unacceptable for delay sensitive applications. The problem is further exacerbated in presence of fluctuating workloads, as in these scenarios the optimal batching level varies dynamically over time, and pursuing optimal performances demands the employment of self-adaptive mechanisms. In this paper we study the problem of self-tuning the message batching level adopting an interdisciplinary approach that employs methodologies from control theory community to optimize the performance of Total Order Broadcast (TOB), a fundamental building block to build dependable distributed systems. Specifically, we introduce an innovative self-tuning algorithm based on extremum seeking optimization principles. We provide theoretical results on its convergence properties and an extensive experimental analysis aimed at assessing the actual effectiveness of the new algorithm in a state-of-the-art group communication system.