Hyperledger Fabric Framework For Blockchain Technology Using Tabu Search And Teaching Learning-Based Optimization Algorithm

Abstract

There has been a steady growth in popularity of the permissioned blockchain platforms recently, among which the Hyperledger Fabric has been identified as one of the most popular distributed ledger platforms. The fabric includes several components like endorsers, committers, and ordering services. Furthermore, it includes different phases of processing in a transaction like the phase of
endorsement, commit phase, validation, and ordering phase. Owing to a large number of components or phases, the Fabric will provide different configurable parameters like the block size, channels, state database, and endorsement policies. Therefore, a major challenge while setting up new and effective blockchain networks is identifying the right value set for the parameters. The Tabu Search (TS) with the Teaching Learning Based Optimization (TLBO) is employed for optimizing a block size with an endorsement policy for various channels in this work. The results of the experiment proved that the results of the experiments had better latency and
throughput, thus resulting in better and optimum performance.