Abstract: The disclosure provides an approach for formally verifying a state machine replication protocol (SMRP) based on a model SMRP, and deploying a distributed system, such as a blockchain, that runs using the formally verified SMRP. The approach provides a verifier that models the SMRP within a model distributed system. Modeling includes modeling actions by model components of the model distributed system so as to transition state of the model SMRP, and then verifying that applicable invariants hold true after the state transition. As long as the model and actual SMRPs are logically equivalent, then launching an actual SMRP based on the model SMRP should preserve formally verified byzantine fault tolerance within the actual SMRP of the distributed system.

Abstract: An algorithm for efficiently maintaining a globally uniform-in-time execution schedule for a dynamically changing set of periodic workload instances is provided. At a high level, the algorithm operates by gradually adjusting execution start times in the schedule until they converge to a globally uniform state. In certain embodiments, the algorithm exhibits the property of “quick convergence,” which means that regardless of the number of periodic workload instances added or removed, the execution start times for all workload instances in the schedule will typically converge to a globally uniform state within a single cycle length from the time of the addition/removal event(s) (subject to a tunable “aggressiveness” parameter).

Abstract: The present disclosure is directed to a novel reconfiguration framework for a BFT SMR system. With this framework, the configuration of both the system itself and the clients of the system can be modified in a live manner (i.e., without taking the system offline) while preserving correct system operation.

Abstract: An algorithm for efficiently maintaining a globally uniform-in-time execution schedule for a dynamically changing set of periodic workload instances is provided. At a high level, the algorithm operates by gradually adjusting execution start times in the schedule until they converge to a globally uniform state. In certain embodiments, the algorithm exhibits the property of “quick convergence,” which means that regardless of the number of periodic workload instances added or removed, the execution start times for all workload instances in the schedule will typically converge to a globally uniform state within a single cycle length from the time of the addition/removal event(s) (subject to a tunable “aggressiveness” parameter).