A Non-Intrusive Multi-objective Task Scheduling Method for JointCloud Environment

The advent of advanced technologies, such as large models, has precipitated a surging demand for computational resources, thereby driving the evolution of cloud computing services from single-cloud architectures to multi-cloud paradigms. The accompanying question is how to enable resources from different cloud service providers to collaborate efficiently. Although existing works have explored multi-cloud scheduling methods, most of these works are centralized scheduling, where the decision-maker can schedule computing resources across all clouds. However, this is nearly impossible given the current situation in which computing resources in different clouds come from various cloud service providers. In response to this challenge, JointCloud, a multi-cloud cooperation architecture, has been proposed, which aims at enhancing the cooperation among multiple clouds to provide efficient multi-cluster services. Following the idea of JointCloud, proposes a multi-objective evolutionary algorithm (MOEA) based method for task scheduling in multi-cluster cloud computing environments, without intervening in the intra-cluster scheduling scheme. In the proposed method, we construct a mathematical model with the optimization objectives of minimizing overall waiting time and load imbalance between clusters based on the actual operation data in China Computing NET (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> NET). In addition, we also develop an MOEA specifically tailored to address this problem. The performance of the proposed MOEA and existing state-of-the-art MOEAs is examined on the proposed problems. Comparison results highlight the promising performance of the proposed MOEA, the specifically tailored algorithm in effectively addressing the multi-cluster task scheduling problem. In addition, we also compared the results of the MOEAs with the results of three classical scheduling methods, the results proved the effectiveness of the MOEA-based method on this problem.