Optimal Strategy for Task Offloading and Resource Pricing Behavior Based on Blockchain

Abstract

End-edge-cloud computing improves efficiency and reduces latency by allocating mobile device tasks between edge nodes and cloud servers. However, task allocation and resource pricing in complex multi-user, multi-task environments remain challenging. This paper proposes an end-edge-cloud task offloading strategy based on blockchain technology, utilizing Stackelberg game theory for resource pricing (BMSGRP). We designed a multi-level Stackelberg game model with cloud servers as the leaders, edge servers as the sub-leaders, and mobile devices as the followers. By proving the monotonicity of the overall system benefit model, we derived the unique equilibrium solution. Then, we use blockchain technology to construct a decentralized resource transaction ledger, recording the task offloading and resource pricing results of various devices. We employ the an improved consensus mechanism based on reputation scoring (mRS-DBFT) to ensure the security and transparency of data and transactions. Finally, we evaluated the performance of this strategy through simulation experiments across various scenarios. The results indicate that our method significantly improves system efficiency compared with local task execution and random offloading.