Web 3.0-enabled edge computing provides a promising foundation for decentralized application (DApp) deployment. However, optimizing execution modes for interdependent task patterns remains challenging due to high computational overhead and blockchain consensus latency. This paper proposes an elastic hybrid architecture for Web 3.0-Enabled Edge Computing Systems (EDGEWEB3.0) that integrates on-chain and off-chain execution. DApp contracts are modeled as Directed Acyclic Graphs (DAGs) and partitioned into dependent task patterns, enabling a structured decomposition of execution. We formulate a DApp task scheduling problem and introduce the Decision Transformer-based Dependent Scheduling (DTDS) algorithm. DTDS employs masked self-attention to capture DAG dependencies and incorporates regularization terms to enforce constraints on service delays, gas costs, and computing capacity. We implement a real-world Web 3.0 testbed based on Ethereum, Goerli, and zkSync. Experimental results demonstrate that DTDS consistently outperforms state-of-the-art baselines in gas costs and service delays, providing a scalable solution for DApp execution in environments.

In the evolving landscape of Web 3.0, deploying and scheduling decentralized applications (DApps) presents significant challenges due to the complexity of heterogeneous nodes, edges, and their intricate interactions. Traditional approaches, particularly graph-based reinforcement learning (RL) methods, often rely on homogeneous graphs, which fail to capture the diverse relationships inherent in heterogeneous Web 3.0 environments. This limitation results in inefficient resource allocation, suboptimal task scheduling, and unclear security requirements. To address these issues, this paper introduces the Heterogeneous Graph Deployment Scheduler (HGDS), a novel framework that leverages Heterogeneous Graph Neural Networks (HGNNs) to model users, edge servers, and DApp tasks within Web 3.0 environments, and incorporates RL to optimize DApp scheduling policies. HGDS captures heterogeneity in Web 3.0 environments by jointly modeling node–edge interactions and heterogeneous edge relationships, enabling the generation of dynamic, task-aware embeddings that integrate both node and edge features. RL is further employed to adaptively optimize scheduling and resource allocation based on real-time network feedback. Experiments on a Web 3.0 testbed show that HGDS outperforms baseline methods by 12.2% in reward, while reducing service delay and gas consumption.

Web 3.0 and Edge computing are inherently compatible, making them an ideal combination for building a secure and efficient distributed service platform to support decentralized applications (DApps). This paper investigates an elastic hybrid computing architecture in Edge Web 3.0, allowing DApp tasks to be executed in a hybrid manner by integrating on-chain and off-chain execution. The principle is to transfer a portion of DApp to an off-chain execution environment, along with an appropriate result verification process, to enhance computing efficiency and reduce blockchain overhead. We formulate a DApp task scheduling problem that jointly optimizes the execution pattern and offloading decision of user tasks. A learning-based DApp task scheduling scheme is designed based on Proximal Policy Optimization (PPO) to minimize the gas cost and service delay of DApps. Particularly, we tailor PPO to handle the hard constraints of service delay, gas consumption, and computing capacity in Edge Web 3.0 by adding regularization terms in the learning objective function. We establish an Edge Web 3.0 testbed based on Goerli, ZkSync, and Ethereum to evaluate the proposed method. The experimental results show that our method outperforms state-of-the-art benchmarks.