An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach

TL;DR

This paper introduces an adaptive multichain blockchain system optimized through multiobjective methods, enhancing scalability, fairness, and stability by dynamically configuring chains based on demand and capacity.

Contribution

It presents a novel multiagent resource-allocation model for blockchain configuration that adapts to changing demands and optimizes multiple objectives simultaneously.

Findings

The model improves scalability and throughput.

It balances decentralization and stability effectively.

Simulations demonstrate trade-offs among key performance metrics.

Abstract

Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability.