A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

Abstract

With the advent of Industry 4.0, the demand of high computing power for tasks such as data mining, 3D rendering, file conversion and cryptography is continuously growing. To this extent, distributed and decentralized environments play a fundamental role by dramatically increasing the amount of available resources. However, there are still several issues in the existing resource sharing solutions, such as the uncertainty of task running time, the renting price and the security of transactions. In this work, we present a blockchain-enabled task assignment platform by performance prediction based on Hyperledger Fabric, an open-source solution for private and permissioned blockchains in enterprise contexts that outperforms other technologies in terms of modularity, security and performance. We propose a model-free deep reinforcement learning framework to predict task runtime in agents current load state while the agent is engaged in multiple concurrent tasks. In addition, we let clients choose between prediction accuracy and price saving on each request. This way, we implicitly give inaccurate agents a chance to get assignments by competing in price rather than in time, allowing them to collect new experiences and improve future predictions. We conduct extensive experiments to evaluate the performance of the proposed scheme.

Keywords

• Blockchain
• cloud
• deep reinforcement learning (DRL)
• resource sharing