![]() ![]() Also inspired by the supply and demand principles of Uber Surge Pricing (blockchain economics) and Power of Attorney model (blockchain scaling), STORE will secure crypto-powered incentives and payments inside of apps. With its Dynamic, Fork-tolerant, and Auditable Proof of Stake-based consensus protocol (DyPoS), STORE will secure free transactions, high throughput, and a decentralized governance system with built-in checks and balances inspired by the U.S. This tool is customized to generate bursts of transactions of different sizes. Since the purpose of the tests is to measure the consensus efficiency and stability of the validator nodes, the application logic is replaced with Tendermint’s tm-bench benchmarking tool. In this series, we examine the behavior of validator nodes when bursts of transactions are sent from multiple clients.įigure 1 shows STORE application architecture, which follows Tendermint's ABCI Protocol.įigure 1 - Illustration of STORE Application and DyPoS Interface These models help identify deficiencies in traditional approaches to consensus, so they can be better addressed in BlockFin. Version 1 of DyPoS is used to model various transaction load patterns to better understand the behavior of the validator nodes. The core consensus engine will be replaced eventually by BlockFin, STORE’s leaderless, fork-tolerant, high-throughput consensus protocol. Version 1 of DyPoS is partially built on top of Tendermint. In this test, we also want to increase the decentralization and see its effect on consensus efficiency. Such a setup offers minimum decentralization. This is the absolute minimum number of nodes required to tolerate Byzantine failure of 1 node. In our previous tests, we used 4 validator nodes to measure the consensus efficiency. This is also called as consensus efficiency. The performance of the consensus engine is measured as the number of transactions processed per second. In this series of tests, we want to measure the performance and stability of the validator nodes forming the consensus engine when the transactions are sent in bursts from multiple clients. So, we need to characterize the behavior of the consensus engine under such circumstances. When transactions arrive continuously but at lower rates, the consensus engine is capable of handling the incoming transactions, but how does it behave when transactions come in bursts? When STORE is used as the payment platform by merchants and app developers, the transactions are likely to come in bursts from multiple sources. The Dynamic Proof of Stake (DyPoS) consensus engine powering the STORE infrastructure is designed to process thousands of transactions per second. STORE’s mission is to become zero-fee, p2p, programmable payments infrastructure for the globe. So when the transaction or burst sizes are too large, the consensus efficiency decreases. The consensus efficiency drops as the transaction volume increases.This setup is typical in a real-world settings. ![]()
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