Distributionally Robust Cost Minimized Edge Semantic Intelligence in the Sustainable Metaverse

With the recent development of the Metaverse, people are more connected with each other. Avatars are used to represent the people, to communicate with one another, and they can build the community virtually. In these processes, a massive amount of data is exchanged between the physical and the virtual world. However, the existing communication technologies are insufficient to support the Metaverse, and the energy consumption of the Metaverse is huge. Therefore, semantic communication is one of the emerging communication paradigms to reduce the size of the data transmitted and reduce energy consumption while maintaining its meaning. Virtual service providers (VSPs) who provide services in the Metaverse can purchase semantic data from the nearby edge sensing units by using two subscription plans: reservation and on-demand. However, in practice, the demand of the VSPs is uncertain due to the variability of the Metaverse. To minimize the cost of the network and prevent over- and under-subscription of the resources, we propose a two-phase stochastic semantic resource allocation (SSRA) scheme. In phase one, a double dutch auction performs a one-to-one matching between VSPs and edge sensing units. The matching is dynamic and depends on the quality of experience (QoE) from the Metaverse users and the semantic data transmission cost from the edge sensing units. The matching changes whenever QoE and the semantic data transmission cost vary. In phase two, we consider the demand uncertainty and matching result from the phase one to formulate a distributed robust optimization (DRO) problem to minimize the operation cost of the VSPs. Using a real-world dataset, simulation results demonstrate that our proposed scheme is fully dynamic and minimizes the operation cost/energy consumption of VSPs in the presence of stochastic uncertainties.