CARTEL: Consensus Adapting Real-Time and Efficient Logging

Consensus algorithms are widely adopted in clustered systems to distribute data efficiently, with Raft being a prominent failover algorithm due to its effectiveness and fault tolerance. However, Raft and other consensus algorithms do not provide timing guarantees, limiting their application to time-critical systems such as industrial control systems, drone swarms, and autonomous vehicle networks, where it is critical to distribute state machines in time and prevent multiple timing misses of data. In this paper, we propose CARTEL (Consensus Adapting Real-Time and Efficient Logging), a novel consensus algorithm that integrates time-predictability into the Raft framework. To achieve this aim, we examine Raft's mechanisms and identify the characteristics that impact the timing of data propagation within a distributed system. Based on these insights, we design CARTEL by developing two mechanisms that address the primary limitations of Raft: (i) CARTEL voting to solve the uncertainty of leader election timing, and (ii) CARTEL node buffer to limit the number of indeterminately deferred data during leader failure. Moreover, we propose how to utilize the mechanisms of CARTEL to ensure time-predictability in leader elections and mitigate the indeterminate nature of data logging during leader transitions, without harming the integrity of Raft. We validate the effectiveness of CARTEL through real-world implementation. The experiments confirm that CARTEL not only reduces the uncertainty of system recovery inherent in the election process (by 65.7% compared to Raft) but also enhances the integrity of the distributed system by guaranteeing timely data logging.