Multiobjective Resource Allocation Strategy for Metaverse Resource Management

With the development of the metaverse, new applications are emerging. The number of computing tasks and data volume are more and more. Cloud-Edge-End computing platforms provide abundant computing resources for applications in the metaverse. It is crucial to optimize the allocation of collaborative computing resources and improve resource utilisation across multiple levels of resource nodes. A four-objective optimization model is developed based on this, considering service delay, energy consumption, load balancing and service cost objectives. In the metaverse, the physical and virtual world interact with each other and connect together in digital twin (DT). A Fog-Edge-Cloud (FEC) collaborative computing framework based on digital twin wireless network (DTWN) is proposed to efficiently manage and integrate these computing resources to provide high quality of service (QoS). In addition, a θ-nondominated sorting based bi-goal evolutionary algorithm (T-BiGEA) is proposed to solve this model. The effectiveness of the T-BiGEA is verified by comparing it with the other algorithms.