Cmrs: A Digital Twin Enabled Workers Recruitment and Task Scheduling Scheme for Future Crowdsourcing Networks Under Precedence Constraints

Abstract

In crowdsourcing computing, a challenge arises in efficiently recruiting workers with unknown computational capabilities to complete tasks with precedence constraints within the shortest possible time. To address this challenge, a digital twin-assisted Combinatorial Multi-arm bandit based multi-round work Recruitment and task Scheduling (CMRS) scheme is proposed in this paper. Firstly, with the help of digital twin technology, we build digital twin models for crowdsourcing tasks and workers in digital twin layer of platform. And we formulate a Multi-round Workers recruitment and Task scheduling with Precedence constraints (MWTP) problem with the object of minimizing task completion time (makespan) to model the above challenging issue in crowdsourcing computing. The MWTP problem can be divided into two sub-problems, the task scheduling sub-problem and the worker recruitment sub-problem. Secondly, in the CMRS scheme, the differential evolution based task scheduling (DETS) algorithm and CUCB-based worker recruitment (CWR) method are proposed to solve them, respectively. Specifically, for the task scheduling sub-problem, we first propose a priority-driven based task scheduling (PDTS) approximation algorithm to produce a primary solution with an explicit approximation ratio. Then a DETS algorithm is proposed to use the solution of PDTS for initialization and generate a better solution that further improves the performance. For the unknown workers recruitment problem, we use the Combinatorial Multi-Arm Bandit (CMAB) framework to model this, and propose CWR method to learn the computational capabilities of workers while maintaining good performance during multiple rounds of worker recruitment by adding a tuning term to balanced exploration and exploitation. Finally, verified by extensive comparison experiments, the CMRS scheme can achieve the best performance of makespan compared with the baselines in most cases.

Keywords: Crowdsourcing, Digital Twin, Combinatorial Multi-Arm Bandit, Upper Confidence Bound, Directed Acyclic Graph, Differential Evolution