Making BFT Protocols Really Adaptive

Abstract

Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an "evaluation process" that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads.

Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

“…Through the machine learning prediction, DABFT dynamically switches the system to the optimal BFT consensus of the present task. The DABFT improves upon the ADAPT (Bahsoun et al 2015) and is similar to it in several ways. Like the ADAPT, the DABFT is a modular design and consists of three important modules: BFT System (BFTS), Event System (ES), and Quality Control System (QCS).…”

“…The DABFT is the fundamental layer distributed consensus algorithm. It improves upon the ADAPT algorithm (Bahsoun et al 2015) and uses deep learning (a branch of Artificial Intelligence) techniques to predict and dynamically select the most suitable Byzantine Fault Tolerant (BFT) algorithm for the current application scenario in order to achieve the best balance of performance, robustness and security. The DABFT is currently the most adaptive distributed consensus solution that meets various technical needs among public chains.…”

Digital Forensics and Privacy-by-Design: Example in a Blockchain-Based Dynamic Navigation System

“…In addition, our blockchain can achieve consensus without computationally expensive proof-of-work, for instance with a Practical Byzantine Fault Tolerance (PBFT) algorithm [26].…”

Sustainable blockchain through proof of exercise

“…Interestingly, the uPoW work supports our idea that matrix-based problems have a high potential to serve as PoWs, and it has a high potential to fit in our PoX model. Finally, ensuring the security of blockchain is also being studied in academia, e.g., [9], [13], [32], [49], [46], following Byzantine fault tolerant (BFT) approaches [10], [31], [22], [27]. However, as shown in [49], BFT-based approaches are not scalable to public settings as in cryptocurrencies, and are thus only used in private blockchain.…”